Intelligent transportation systems—Enabling technologies

Pergamon

Math,l. Comput. Modelling Vol. 22, No. 4-7, pp. 11-81, 1995 Copyright@1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0895-7177195 $9.50 + 0.00

0895-7177(95)00127-l

lrntelligent Transportation SystemsEnabling Technologies A. GARCIA-ORTIZ Decision & Control Technologies, Advanced Development Electronics & Space Corp. Mail Stop 4363, 8100 W. Florissant Ave. St. Louis, MO 63136-1494, U.S.A.

Center

S. M. AMIN Center for Optimization and Semantic Control Department of Systems Science and Mathematics Washington University, #l Brookings Drive, Campus Box 1040 St. Louis, MO 63130-4899, U.S.A. J. R. WOOTTON Advanced Development Center Electronics & Space Corp. Mail Stop 4410, 8100 W. Florissant Ave. St. Louis, MO 63136-1494 Abstract-Intelligent transportation programs take many different names throughout the world; in the United States it is ITS, in Europe it is Prometheus, and in Japan it is AMTICS and RACS. All of them share very similar objectives, i.e., the development of advanced Traffic Management Systems, Traveler Information Systems, Vehicle Control Systems, Commercial Vehicle Operations, Public Transportation Systems, and Rural Transportation Systems. Several key technologies stand to serve the synthesis of each and every one of t.hese objectives. These technologies are: Digital hfaps, Computers, Path Planning, Human Factors, Sensors, Communications, Vehicle Control, and Traffic Control. This paper discusses each one of these subjects in enough detail to provide the reader with an introduction to both the technology and its state-of-the-art. In addition, the paper discusses socio-politico-economic issues associated with the implementation of the various programs. We believe that this highly neglected subject will serve to temper the development and deployment of these programs. Keywords-Intelligent transportation systems, Digital maps, Computers, tors, Sensors, Communications, Control, Socioeconomic.

Planning, Human fac-

INTRODUCTION The subject of intelligent transportation systems takes many different names throughout the industrialized world. In the United States it takes the name of ITS, in Europe it is called Prometheus, and in Japan it is known as AMTICS and RACS. Irrespective of its national identity all of these programs share very similar goals, and the technologies that will help achieve those goals are also closely related. This paper discusses the basic set of technologies that will enable Throughout this paper we will use the U.S. model, the fulfillment of these national programs. i.e., ITS, as the basis for discussion. In the U.S., the services to be offered by the ITS initiative have been categorized in two The first one initially subdivided the subject into the ways by the ITS America organization. 5 categories

of transportation

services provided:

Advanced

Traffic Management

Systems Typeset

11

(ATMS),

by A@-TEX

A. GARCIA-ORTIZ et al.

12

Advanced Traveler Information

Systems (ATIS),

Advanced Vehicle Control SysteFs

(AVCS),

Commercial Vehicle Operations (CVO), and Advanced Public Transportation Systems (APTS); a sixth category, Advanced Rural Transportation Systems (ARTS), was later added. In 1993, this categorization was changed to that shown in Table 1; and in all likelihood this list will also be revised in the future. Irrespective of the categorization, several basic technologies stand to serve the synthesis of ITS. Based on our involvement with various aspects of ITS, both at an academic and at an industrial level, we have identified these enabling technologies to be those listed in Table 2. Table 1. ITS user services. Pretrip Travel Information

En-route Travel Advisory

En-route Transit Advisory

Traveler Services Information

Route Guidance

Ride Matching and Reservation

Incident Management

Travel Demand Management

TrafIic Control Commercial Commercial

Electronic

Vehicle Preclearance

Vehicle Administrative

Commercial

Services

Automated

Payment Services

Roadside Safety Inspections

On-board

Safety Monitoring

Fleet Management

Table 2. ITS enabling technologies.

In the sections that follow, we discuss each one of these technologies in some detail. tive is not to provide exhaustive coverage, but rather to give an introduction to the and provide some insight into the state of the art. In addition to the technology we have included a discussion of the socio-politico-economic issues associated with mentation of ITS; we feel that this highly neglected subject will serve to temper the development and deployment.

DIGITAL

Our objectechnology discussion, the impletechnology

MAPS

Introduction As we looked for a set of basic technologies which find widespread application throughout the field of ITS, we established that one such technology is digital maps [l-12]. In this context, digital maps find application in traveler information systems, traffic management systems, commercial vehicle operations, public transportation systems, and rural transportation systems. The reason for this is rather obvious-a map is a graphical representation of natural and man-made objects that surround us. As such it provides a “natural” mechanism for the visual presentation, and quick assimilation by the viewer, of situational information such as traffic density and speed, and travel routes. As a matter of fact, very few graphical displays achieve the information density possible with maps; a typical USGS topographic quadrangle is estimated to contain 250,000 bits of information per square inch. As the adage goes, “a picture is worth a thousand words.” The use of maps spans many many centuries, and their availability is responsible for the exploration of the World by the Portuguese, the Spanish, and other European nations during the 15th, 16th and 17th centuries. Early maps were drawn on clay tablets, and in some cases engraved in stone, e.g., the Yii Chi Thu of llth century A.D. China. Later they were drawn on softer and lighter materials such as papyrus, vellum, and parchment and, more recently, on paper. With the advent of digital computers came the creation of digital maps.

Enabling

Digital The

13

Maps two key technical

and (2) cartography. special

fields which shape

Within

significance.

analysis

digital

map technology

the field of computer

They are: (1) computer

science,

graphics,

three

are:

(1) computer

distinct

(2) database

technical

management,

science

areas are of

and (3) spatial

algorithms.

Computer

graphics

The contents data

Technologies

of a digital map can be categorized

and (2) nongraphic

re-create

data.

Graphic

data

into two fundamental

are digital

the map either on a display device or on paper.

to depict

map features

pixels, and symbols. qualities,

and their

Nongraphic

or relationships

associated

descriptions

data types:

Six types of elements

amlotations,

namely

points,

and geographic

locations,

used to

are typically

lines, areas,

data on the other hand are representations

of map features

(1) graphic

of map features

used

grid cells,

of the characteristics,

often referred

to as feature

attributes. Graphic features

(data are stored either as vector data or as raster data.

as points,

to represent represent

lines, or polygons

building

states

and geographic

locations;

information,

use is made of a topological

between

of lines are recorded

in the graph This spatial The

The arcs may be undirected

same

is a chain.

Closed

by polygons. information

A sequence

areas bounded

Routing

algorithms

also supports

approach.

arc implies that

arc specifies

polygon

such as a political information

overlaying

the end

The lines represent

An undirected

A directed

may

of map features

of arcs and nodes connecting

by a chain,

may be used

In this approach,

on a graph.

or directed.

direction.

geographic

and polygons

the logical relationships

use topological

efficient

Points

or boundaries;

as nodes or vertices

two nodes may occur in either

or flow, e.g., a one way street.

represented

roads

To record and manipulate

arcs or links on the graph. of motion

as a series of .r, y coordinates.

lines may represent

or counties.

points and intersections movement

stored

Vector data represent

a direction

any two nodes subdivision,

are

to select vehicle routes.

procedures

that

are us4

for

analysis. descriptive

attributes

The data are usually

of the vector

available

layer in a map, such as streets, for the Atlanta the county

metro

boundary

in coverages. buildings,

area; Figure

data features a coverage or counties.

2 illustrates

are stored

in a feature

being a digital Figure

1 shows a county

the result of overlaying

The coordinate

“cm 22-U-C

table.

of a single

boundary

a city boundary

layer

layer on

data.

Figure 1. One particular type of data coverage available is county boundaries: here are several counties in the Atlanta metropolitan area.

coordinates9

attribute

representation

systems

typically

values are expressed

used are of two types:

in many potential

Cartesian

map projections.

shown

and latitude/longitude. The most commonly

The used in

A. GARCIA-ORTIZ et al.

14

\

Figure 2. Addition of a city coverage starts to yield a picture of the population distribution in the area. are the State Plane Coordinates (SPC) and the Universal Transverse Mercator (UTM) projections. The latitude/longitude coordinate system records angular measurements relative to the equator and the Greenwich Prime Meridian, and is compatible throughout the world. Because of this, most data providers deliver the data in this coordinate system; conversion to the U.S.

other projections is left to the user. Raster data divides a geographic area into a uniform, rectangular grid of rows and columns. The intersection of a row and a column is referred to as a cell. Each cell corresponds to a geographic area on the surface of the Earth. The resolution of the raster data depends on the size of the cells, so it is important to select an optimal grid size to suit the application requirements. Unlike the vector structure, where attributes are assigned to actual features, attribute data in the raster data structure are assigned to the individual cells. Two types of raster data are available: grids and images. In the case of grid data, attribute information is assigned to each cell; i.e., each grid point carries with it a value that defines the Earth’s surface at that location. Grids are typically used to store elevation data in the form of digital terrain models (DTM) or digital elevation models (DEM). In the case of image data, the cells are called pixels. The size of the pixel typically depends on the imaging technology used to acquire the data. The source of the data may be either imagery collected by a satellite and corrected for motion, optics and projection, or imagery digitally scanned from a hardcopy map. Vector data in general requires considerably less computer mass memory for storage than raster data does, and with recent improvements in graphic display hardware, it is enjoying considerably more use in ITS applications than raster data. Database

management

One notable characteristic of graphic data is their large memory requirements, which demands efficient search and retrieve algorithms. To handle these extremely large databases, use is made of specialized data structures. The data storage models used are hierarchical, network, and relational. These models provide facilities for describing the logical structure of a database using trees, collections of nodes and links, and tables. All of them present the user’s view of a schema in terms of a record structure. The hierarchical data model is the oldest of the database models. The model stores data in a structure that is based on one-to-one and one-to-many relationships; i.e., a hierarchical database consists of a forest of trees. Each instance of a record type can only have one parent record. The simplicity of the hierarchical data structure makes it attractive; however, combining this simplicity with the completeness of the relations generally required proves to be cumbersome. The result is that the database product tends to be complex and restrictive. A notable fact about

Enabling Technologies

the hierarchical

model

is that

This lack of commonality that

users of one hierarchical

In the network

model,

model one-to-one,

there system

records

one-to-many,

several parent record types, of a given record

is no common

has prevented

type.

domain

its reliance by named

records.

and a given record instance model

relationships

among entities

type is designated The relational

relationships

of the model

underlying

is to determine

attributes.

columns.

The

the logical description

model

of a data object.

set of tuples of the same scheme. a model of simple syntactic

Spatial Spatial

flexibility

analysis

map.

example

e.g.,

Avenue street

Topological

as members.

structure

to attributes.

The

and the

of rows and

Each t,uple represents

scheme is a set of attributes semantic

simplicity,

be in the database

and a relat,ion is a

model has evolved into

capabilities.

would reveal,

is readily

The relational

model

Adjacency

An adjacency

falls between

of points,

all the links that defines

share

what interstate

would reveal, for example.

query would be:

software

what

postal

for example,

perform

packages

and perform

bines features,

and their

layer is overlaid

is overlaid

such as MapInfo there

associated

lover a satellite

Address

efficient

matching

represent

features

node,

highways

pass through

that

an

for the

or geocoding

County,

Texas?

a given region.

to zip code 63121?

lie near a line.

included

link

a polygon.

Proximity

A proximity

with geographical

(query Spatial

information

and ArcView.

annotation, layer that

about

a specific

fall within Harris

refer t,o

algor’!thms

radius of the cargo pickup location?

are typicaily

matching

analysis

algorithms

or geocoding. from multiple is overlaid

the geographic

service delivery

or whether

the set of regions that surround

which

A graphic

are used to generate overlay operation

map layers.

over a zipcode

image of the St. Louis h1etropolitan

to the viewer information

used to establish

An

Such

database

Connectivity

or the links that

codes are adjacent

are spatial

address

over a street

a specific

the set of points

these functions

to the above,

overlays

Avenue.

in the street

lines, or polygons.

an area of interest,

would be: what public scales lie within a 5 kilometer

In addition

provided

in the

along one side of a street

201 and 205 Broadway

from the data

and proximity

that

would reveal,

tools that

sequence

contained

or topological

on the other hand, are of a more complex nature and typically

query would be:

algorithms

algorithms

Avenue

data

simple

segment.

for example,

A connectivity

with which to analyze

would be the address

determined

adjacency,

is part of the polygon

the means

fall into one of two categories:

relation

relationships,

the connectivity,

that

The

One record

of data dependencies.

can be viewed as a matrix

A relation

provide

203 Broadway

Broadway

relate

should

and the columns

units with considerable

spatial

relation

tomer

data items.

It, offers conceptual

Since its advent in 1970, the relational

relationships

adjacency

graphic

are represented

and grouped

in the range of queries that can be handled.

algorithms

Spatial

of a simple

segment;

system

from the rest is

analysis

digital

analysis

apart

Entities

At its core is the theory

to tuples,

The

the owner and one or more members.

how many relations

relational

The rows correspond

offers greater

sets this model

elementary

model.

the model was placed in

by named sets among the record types.

between

foundation.

files that

record instances

the hierarchical

relationships.

model finds its origins in the work of Codd [lo].

by a solid theoretical

objective

than

as the set type owner, and one or more record types are designated

The set is used to capture backed

that data

have as attributes

are represented

in conceptual

Data Base Task Group;

sets to capture

of this is system.

A given record type may have

may have several parent

is more flexible

The one characteristic records

hierarchical

together

relationships.

systems in ge:neral.

the implication

path to another

of the same type are grouped and many-to-many

information

These

for hierarchical

of standards;

have no migration

The network

in 1971.

on named

heritage

the development

model is ba.sed on the work of the CODASYL the public

15

Area.

dispersion

For example, boundary

layer.

The resulting

of its customers,

coma custhat

map would and could be

routes.

algorithms

offer the capability

or events on a map containing

a street

to generate

network.

point

A geocoding

locations algorithm

A. GA&A-ORTIZ

16

et al.

estimates an address location on a map by interpolating the given address between two street segments using the address range assigned to each street segment. Although geocoding does not give the exact address location, it normally gives a more than adequate answer. Geocoding is based on the assumption that: (1) addresses are numerically sequential, and (2) even and odd addresses occur on opposite sides of a street. This is in general the case in the U.S.; however, anomalies do exist which can lead to geocoding problems. The process does not work in other countries, a case in point being Japan, where the lack of an addressing scheme makes such an address matching technique impossible. Cartography Before there were digital maps, cartographers objective

created hardcopy maps, their primary technical

being to create highly accurate maps. Since today’s digital maps are no more than an

evolutionary step in cartography, the need for accuracy still remains. The most commonly used measure for the accuracy of the graphic features on a digital map is positional accuracy. Positional accuracy is a measurement of the variance in the position of a map feature from its true position. The measurement can be either relative or absolute. Relative positional accuracy measures the position accuracy of a feature on a map relative to the position of other features on the same map. Absolute positional accuracy measures the position accuracy of a feature on a map relative to its true position on the surface of the Earth. The map scale is very important to the measurement of positional accuracy. The scale is typically expressed as the ratio of measurement units on the map to measurement units on the Earth. It is often stated as uniform parts such as l:lOO,OOO, where, for example, 1 centimeter on the map is equal to 100,000 centimeters on the Earth surface. As the scale gets smaller, the positional error becomes larger. Scale is also an important factor in the display of maps. As the map scale becomes smaller, the density of data per unit of area becomes greater. As significant changes in scale occur, it is typically necessary to remove detail to produce a map that is visually pleasing. The converse situation is also true; a significant increase in scale requires added detail. Another important measure of the graphic accuracy of a digital map is completeness. This is especially true for ITS applications in vehicle navigation and route planning. Two types of map completeness errors have been identified in the literature: (1) geometry errors, and (2) topology errors. Geometry errors are those that relate to the positional accuracy of the road data in the map. Their primary impact is on the correct placement of a vehicle on a map using latitude, longitude coordinates as determined by any of several automatic vehicle location methods, e.g., GPS, Loran, or dead reckoning. When the error is large, the vehicle is positioned off the road. This type of error is critical to applications that employ raster street maps since there is no way of associating the vehicle location to the street it is traveling. Figure 3 illustrates the impact of geometrical errors on a vehicle navigation system. Topology errors refer to errors in the connectivity of the street segment data in the map, and they include both missing and nonexisting streets. In the area of vehicle navigation, this type of error leads to inaccurate map-matching where such a technique is used for map navigation. In the TravTek project, Orlando, Florida, map matching errors were handled by providing the driver with a push button that allowed him/her to correct the vehicle location by hopping streets on the map display, one at a time, until the vehicle icon was placed over the correct street segment. Topology errors have perhaps their biggest impact on vehicle routing algorithms since they lead to the generation of routes over fictitious roads. This results in inconvenience to the driver which in turn impacts the acceptance of vehicle navigation systems in a manner that is perhaps out of proportion with the level of error occurrence, but which nevertheless points to the importance of using up-to-date road databases. A key source of topological errors is the use of subdivision builders’ plot plans in the creation of digital maps; in many cases, all of the originally

Enabling Technologies

17

Figure 3. Geometrical errors become evident when GPS data are overlayed onto a street coverage; the arrow points to a gross discrepancy between the street data (line segments) and the GPS data (dots).

planned

streets

were never developed.

files also tend to suffer considerably the database undertaking

is the only way to correct in terms of man-hours;

and St. Louis County the GDT

Street

street

Geographic

metropolitan

database

databases

from topological these errors.

for example,

which are based solely on TIGER

errors.

In the U.S.,

the GDT

area alone contains

is updated

street

verification

this represents database

over 80,000

data

and update

of

a monumental

for the St. Louis City

street

segments.

Nationwide

every three months.

Data

The key to the success of any digital map application may come from a variety as diverse

as vehicle

of sources,

routing,

use maps of general Specific

Periodic

transportation

geographic

features

planning,

application

may need data about

The table below shows some typical

applications

Optimal

l

Vehicle Routing

.

.

Vehicle Navigation

.

Transportation

l

Analysis

The data

analysis.

may be

Most of them

boundaries,

to the application. traffic density,

will

and place names. For example, and postal

a

codes.

data applications. Demographic Data

Consumer Data

Geopolitical Boundaries

. .

Dispatching

data.

The applications

and the data used.

.

Routing

Emergency

political

locations,

Postal Codes

Streets

and business

vary according

customer

Table 3. Geographic

‘Tas.k\Data Needs

and private.

such as streets,

data needs beyond these basic features

particular

lies with the geographic

both governmental

. .

.

Streets Streets

and highways

it easy to recognize address Street

ranges.

Census

map features

developed

Encoding

way to obtain U.S.

Other

map data

Geographic

on maps because

travel

routes,

by the U.S. government,

information

they

and business

such as rivers and railroads

and Referencing)

detailed Bureau

are key features

neighborhoods,

about

a small

area.

Line Graphs) The

the first comprehensive

orientation Streets

are often included

such as TIGER

and DLG (Digital

in 1990, constitute

provide locations.

TIGER digital

(Topological

and make

often

include

as well. Integrated

files, offer an inexpensive files, introduced street

mapping

by the effort

in

A. GARCIA-ORTIZ et al.

18

the U.S., covering 100 percent of the nation. The database contains digital data at a scale of l:lOO,OOO for every street and road in the nation, the range of street address numbers located along each section of every street in the 345 largest urban areas, all railroads, and all significant hydrographic features. The TIGER data also covers U.S. territories. To generate the TIGER files, the U.S. Census Bureau combined the DLG files with its own DIME (Dual Independent Map Encoding)

files introduced in 1970. TIGER

features include roads, railroads, pipelines,

powerlines, airports, lakes, rivers, creeks, canals, state and county lines, and census tracts and blocks. The files are updated as the Census Bureau compiles census data. Street data coverage is available for the entire U.S., by state, by county, by metropolitan area, by census area, and by postal code area. The accuracy of the maps depend on the location: in rural areas which account for 98 percent of the U.S., the maps reflect U.S. Geological Survey (USGS) accuracy standards of 167 feet average; in urban areas no accuracy standards have been defined. The DLG data are a product of the USGS. Each road, river, railroad or other geographical feature is represented by a line that is described as a series of x, y coordinates. These coordinates describe the position and shape of the map feature. The lines that make up a feature have associated attribute codes that describe the feature. DLG maps are available for the entire U.S., except for the State of Alaska, in a l:lOO,OO scale. DLG maps reflect the USGS official accuracy standards, although most are about 15 meters. At scales larger than 1:24,000 vector representation inconsistencies such as rough curves begin to appear. For tasks like travel assistance, transportation planning, computer-aided dispatch, and address matching the use of enhanced street data is necessary. If the task calls for cartographic appeal or right of way information then street data offering variable width, double lined streets may be required. U.S. producers of enhanced street databases include Geographic Data Technology (GDT), American Digital Cartography (ADC), Etak, and Navigation Technologies. GDT supplies seamless, nationwide street coverage with quarterly updates; Etak and Navigation technologies supply high quality street coverage for selected metropolitan areas; American Cartography supplies double lined street coverage. Figure 4 depicts the typical appearance of a U.S. street coverage. The source of these databases include the DLG files produced by the USGS, the TIGER files from the USBC, U.S. Postal Service (USPS) data, private address files, aerial photography, and USGS topographic maps.

Figure 4. Street data coverage for the U.S. is quite comprehensive; shown here is a portion of the St. Louis County street database from Geographic Data Technology.

GDT maps resemble the TIGER maps graphically, however, they offer twice as many address ranges. The maps are constantly updated using USPS data for new addresses, including street names in new subdivisions. The maps meet the USGS accuracy standards in rural areas, and have varying accuracy in urban areas. All streets have current 5-digit ZIP codes; a ZIP+4 database is also available.

EnablingTechnologies

19

ADC offers maps from various sources which are enhanced through the use of variable line width, double standard line width, or double variable line width.

Features are positioned by

classification on separate layers. Et,ak ma.ps have many more shape points than DLG or TIGER files, thus smo0thin.g the highway interchanges and other curves. As the number of shape points increase in number so does the file size; for the same geographic area, Etak maps require more disk space than GDT maps. Features in these maps are typically accurate to 7 meters from their actual position. The data. are based on source materials at a 1:24,000 scale in urban areas, and l:lOO,OOO in rural areas. In Europe, Philips and Bosch are responsible for the original effort of developing a common European digital road map. The Digital Electronic Mapping of European Territory (DEMETER) project hacl as its goal the development of specifications for car navigation, road databases’. The work continued under the Dedicated Road Infrastructure for Vehicle Safety in Europe (DRIVE) project under a consortium consisting of Philips, Bosch, Daimler-Benz, Renault, Tele Atlas, and Intergraph. The goal was to define a set of basic data covering all of Europe which could be used as the basis any number of traffic related applications. Currently, the data supplied by Tele Atlas is at a scale of l:lO,OOO or better, and covers all of the European Union (EU). The data includes streets, rivers, railways, and administrative boundaries. In Japan, the Japan Digital Road Map Association (JDRMA) was formed in 1988 to establish digital road map standards, and a digital road database of Japan. An initial database was developed in a 1:50,000 scale; this database is now being refined to a 1:25,000 scale. By early 1993, 50 percent of the country was covered at the higher resolution. The largest application of this database has been for vehicle navigation. About 130,000 vehicles are currently equipped with a vehicle navigation system. The key users are the police and fire departments, ambulance services, and taxi companies. Boundaries Geographic boundary data include boundaries for areas such as census areas, postal areas. and political and administrative areas. Political and administrative boundaries represent cot mtries in the U.S., provinces and their legal subdivisions. Every country has its own system-states in Canada, and cantons in China. In the U.S. the states are further divided into counties for administrative and political purposes. In Alaska, counties are called boroughs, and in Louisiana they are called parishes. Postal areas, such as the U.S.‘s 5-digit ZIP code and ZIP+4, enable address matching and geolocation of customer lists. ZIP code areas exhaustively divide states into regions serviced by specific post offices. The ZIP+4 code takes you even down t,o a specific street segment. or a building floor. In the U.S., the ZIP code boundary data are available for the entire country. The basic source of the data is the USPS and the USCB. The data are typically updated mont,hly by t,hird party vendors using USPS data, proprietary data and private sources. A similar system 1s used by Canada and the United Kingdom. Applications Digital maps find application in traveler information systems, traffic management systems, commercial vehicle operations, public transportation systems, and rural transportation systems. In these areas, they are used for data display, navigation, and fleet management. A considerable number of applications are reported in the literature, too many in fact, so we will only touch on the subject, just enough to reveal the breadth and depth of digital map applications. Data display The display of data is perhaps the most straightforward application of digital maps.

20

A. GARCIA-ORTIZet al.

Dispatching applications allow companies like Pizza Hut and Yellow Cab to establish districts and send out vehicles in a more efficient manner in response to customer calls. Dynamic dispatching is used to dispatch emergency vehicles and parcel pickup services. In its simplest form, the technology helps by displaying the call for service graphically on a map. Navigation Vehicle navigation systems are slowly being introduced into the world marketplace

with the

hope that in the very near future they will help alleviate traffic congestion and the associated environmental pollution. The systems will be used in passenger, emergency service, and utility repair vehicles.

The vehicles carry an on-board computer that show the vehicle location and

plots a route, avoiding trouble spots. As the technology progresses, these systems will also avail themselves of up-to-date traffic information to be used in identifying the quickest route. One of the earliest navigation systems deployed in the U.S. was the Navigator, developed by Etak, Inc. In this system, navigation is based on map matching; i.e., the vehicle location is based on the correlation of a time history segment of the vehicle motion with a street database. The street database available covers the U.S. and a significant portion of the developed countries. Distribution of the Navigator is now with Robert Bosch of Germany. Of a more recent debut in the U.S. market is a system being put together by Rockwell International and Zexel USA Corp. for the General Motors’ Oldsmobile 88 LSS, luxury sports sedan. Sony and Volvo offer similar systems in Japan and Europe, respectively. Common to all of these units is the use of vehicle location data provided by the Global Positioning System (GPS). This system uses a constellation of satellites of known position, and Earth-based sensors. By simultaneously reading the position of several satellites the location of the sensor can be determined very accurately. Typical accuracy for commercial applications is 25 meters; with the use of differential GPS the location accuracy can in some cases be within 5 centimeters. This compares with the Loran-C accuracy of 300 meters. Vehicle navigation will be by far the largest user of digital maps. As a measure of the market potential, in the U.S. alone there are close to 200 million vehicles, and projections are that in Japan some 350,000 on-board computer navigation systems will be sold this year. Fleet

management

By some estimates, US$500 billion of the U.S. gross national product are spent in logistics. The bulk of this is spent by several thousand companies which for the most part operate fleets of twenty five or more vehicles. Studies have shown that through route optimization and scheduling these companies can save between 5 and 15 percent of their operating cost. Two specific operational issues are of interest under fleet management: the location of the vehicles in the fleet, and the dispatching of the vehicles in response to customer needs. In this regard, digital street maps are used for three things: (1) the geocoding of delivery addresses before a routing algorithm is applied, (2) the use of the street network to estimate driving time along the route, and (3) the analysis of the network by the routing algorithm to identify the shortest, preferred route.

fastest, or

Commercial delivery services like Federal Express Corporation and United Parcel Service, Inc. are using digital maps for a variety of applications. Companies like Pizza Hut and Yellow Cab use maps to establish districts and send out vehicles in a more efficient manner in response to customer calls. As a result of the need to improve the vehicle dispatching process, a new service sector has emerged. Roadnet Technologies, Inc., for example, sells vehicle routing and scheduling software to food, soft drink, and wine and spirits distributors that make large deliveries. Roadnet was acquired by the United Postal Service in 1986. Other examples include LOGIROUTE Computer Systems, Inc., Montreal, Canada. Its product, GeoRoute, combines a street database with

Enabling Technologies

21

route optimization procedures to generate maps and route itineraries for, amongst others, postal delivery, garbage collection, and goods delivery in Canada and the U.S. The available routing functions include shortest path routing, travel-distance

perimeters around selected points, point-

to-point routing, and link routing. Another example is Bowne Distinct, Ltd, Columbia, Maryland. Its product, RouteSmart, also combines a street database with routing algorithms, and has been applied to the solution of street routing and scheduling problems in solid waste collection, utility meter reading, and street sweeping. allocation,

The program addresses the design of equitable

the building of routes, the establishment

paths on a street by street basis. Both point-to-point

of schedules, and the enumeration

workload of travel

and neighborhood routing are handled.

In the U.S., the ADVANCE project in Chicago, IL is testing out vehicles with on-board navigation syst#ems that direct drivers around construction sites, accidents, or high traffic spots. An information center collects real-time data from the cars to compute travel times which are then relayed back for use in route planning.

The project calls for 5000 “probe” vehicles to be

equipped. A paper by Sen also appearing in this issue describes the methodology being followed by the ADVANCE project for generating traffic data updates. The largest provider of fleet vehicle location services in the U.S. is QUALCOMM with its product OmniTracs. The company has a customer base of about 40,000 vehicles. Rockwell International has also developed a vehicle location system called FleetMaster, and Northrop Grumman has developed a system called the Intelligent Vehicle Management System to track, dispatch, and direct commercial vehicles over a large area. The latter system is in use by the Seoul Metropolitan

Police Department.

Closely associated to fleet management is stolen vehicle recovery. Several companies in the U.S. provide vehicle location services for the recovery of stolen vehicles. The most active in the market is PacTel, which uses its paging network and a transmitter installed in the vehicle to establish the location of a stolen vehicle. The location coordinates are then overlaid on a digital map and reverse-geocoded to obtain a street address. Competing companies make use of the cellular A recent entrant into this market is Connrak, phone system to obtain the vehicle location. St. Louis, MO. This company places transceivers on the existing telephone network infrastructure which work. in concert with a unit mounted in the vehicle. Their system is used for both fleet management and stolen vehicle recovery.

COMPUTERS The revolutionary promise that is the anticipated outcome of ITS and its European counterpart would not be feasible were it not for the advancements already being achieved, not only in the generic area of computer technology, but also in the areas of semiconductor technology and generic algorithm development. These achievements for the most part are being achieved outside of the Transportation Industry, and these technologies will continue to advance independently of this industry. It is only relatively recently that electronics first found their way into vehicles in the form of dedicated semiconductor integrated circuits that took care of engine control. That step was not without some trepidation in respect to the public’s fear or conception at that time. Today, electronic engine control is expected by the public. A parallel path can be expected for the future with regards to such areas as electronic route guidance, collision avoidance and even platooning.. The key to all of these advancements is the continued parallel evolution of smaller, faster, more powerful computers which are competitively priced. Before going too deeply into these subjects, we should reflect that much of the revolution that is going on, either inside the vehicle or exterior to the vehicle, is because we have the necessary algorithms to afford improvements over the individual human’s current performance, and we can affordably implement these on today’s readily available hardware, principally today’s microprocessors.

A. GARCIA-OFLTIZ et al.

22

Inside the vehicle, the basic tasks, e.g., route guidance, situational awareness, platooning and collision avoidance, rely on advances in fundamental technologies which include image processing, artificial

intelligence,

fuzzy logic, neural

151. The development and medical

related

networks,

of these technologies problems.

adaptive

control

has been accelerated

Most of these solutions

require

and semantic

by other

networks

[13-

fields such as defense

a level of computational

sophis-

tication that a decade ago would be inconceivable to be undertaken outside the computational facilities of a research center. Computers and more specifically computer microprocessors have, because of market stimulus, continued to exceed all predictions with regard to clock speed and We have lived through the revolutions made possible processing power. of the Intel 80386 and the 80486 microprocessors, and are at the advent Pentium

microprocessor.

from desktop and smaller

through devices

The result portables

integrated

and laptop to palmtop

with increasing

increasing leverage of processing memory. The ready availability

of these power,

circuits computers

yet maintaining

integrated circuits is matched of inexpensive large memory,

by the development of the impact

has been a steady

which are progressively

affordability

of the

progression

and ruggedness.

smaller The

with equal advances in solid state both magnetic and more recently

optical memory, allows heretofore unheard of large databases, such as required for GIS applications, to be stored in compact and affordable memory media such as CD ROM. In addition to these general purpose machines, there are corresponding improvements in coprocessor integrated circuits, DSP (digital signal processor), and both dedicated fuzzy logic and neural network integrated circuits, e.g., ETANN. The advances in multichip modules (MCM) techniques allows particular arrays of combinations of these various integrated circuits to be assembled for a dedicated purpose such as an image processing collision avoidance system. The availability of general purpose single board computers puts a tremendous processing power within the small physical constraints of a vehicle. The fact that these advances are being made outside or independent of the transportation industry is irrelevant; what is of significance is that this leverage of technology into the transportation industry is only possible because of relatively inexpensive computational power readily available today. When one looks beyond the individual vehicle, to such areas as fleet management and traffic With traffic control, again one sees the leverage that modern computer power can achieve. control there are two principal areas where we see the immediate impact of this technology, namely, (1) sensing the traffic flow, and (2) managing or controlling that flow. For sensing the traffic, there are a number of different techniques, but the nonembedded types of sensors, i.e., radar and imaging sensors, all require intensive signal or image processing capability. Again the availability of single board, real time, dedicated DSP processors makes it feasible to consider large numbers of dedicated sensors capable of detecting traffic flow, and classifying it into its various categories, e.g., cars, vans, and trucks. On the aspect of managing traffic flow at control centers, the availability of powerful and highly reliable workstations will place more emphasis on algorithm research than on the question of finding a computer powerful enough to implement the solutions in real time.

PATH

PLANNING

Introduction In-vehicle path planning is central to the development of advanced traveler information systems (ATIS). An enabling technology for the navigation system is the availability of and improvements to vehicle location systems. These include: Global positioning systems, mobile communication networks, dead reckoning technology, Argos, Loran-C, Omega, Lojack [16]. Earlier vehicular navigation systems utilized dead reckoning using gyros to count wheel revolutions in order to determine the vehicle’s current location, with an error of less than 2 percent of distance traveled, and an update interval of one second. An example of such an approach was Toyota’s

Enabling Technologies

23

Electra Multivision system, first introduced in 1987, that used both dead reckoning and a collection of digitized maps on a compact disk. More recent routing systems use an in-vehicle GPS receiver to estimate the vehicle’s location on a digitized map to within about fifty feet, with an update interval of one second. Such navigational systems calculate the fastest or shortest route between two locations. A typical vehicle navigation system contains several modules: sensors for heading, distance traveled, and geographic location; digital map database; data transceiver; user inputs and control commands; information display, audio/graphic outputs [16,17]. Examples of such GPS routers include: Blaupunkt’s Berlin RCM 303A mobile information center, designed by Blaupunkt of Robert Bosch GmbH, Hildesheim, Germany; General Motor’s TravTek; Oldsmobile’s Guidestar; Telepath 100 by Delco Electronics; and Sony’s NVX-F160 which utilizes a GPS with digital street maps developed by Etak Inc. In this section, we provide an overview of methodologies,

theoretical formulations and im-

plementation of route planning algorithms which provide the driver with “optimal” routes in a timely and memory efficient manner. These have been extracted from network optimization and search and planning applications in artificial intelligence [18--381. Data Structures for Path Planning [19,39] In a shortest-path vehicle router, the objective is to select and implement vehicle routing algorithms for inter and intra-city route planning while addressing the following issues: (a) (b) (c) (d)

shortest distance, quickest route, vehicle traffic restrictions, and driver’s travel preferences.

For this problem, the street/highway databases are typically organized as a set of nodes (vertices) and arcs (edges). An example of an ArcInfo formatted, GDT street database for St. Louis is given in Figure 5

~~

Figure 5. Sample input data structure from a St. LOUIS City street database

Given a list of m vertices and n edges, in general, there are several ways to represent such a road network for computer processing. These include adjacency lists, edge lists, forward star, vertex adjacency matrix, and a vertex-edge incidence matrix. An Adjacency List is a linked list which maintains a pointer to a list of vertices that are adjacent to a given vertex. An array is maintained for each vertex. Such lists exploit the sparsity of graphs; however, it is difficult to locate a specific edge. An Edge List is a listing of starting and ending vertices, e.g., Figure 5. It requires only 2n memory storage locations to represent a graph, and it is very useful for sparse networks. A Forward Star is a data structure which maintains two arrays, one for the pointers in a vertex list and a second for a combined adjacency list. For example, to find all edges incident to node 2, one would first find the pointer corresponding to 2, and the pointer corresponding to the next vertex. Star data structures are not as easily updated as adjacency lists; however, they better exploit the sparsity of graphs, and use less memory storage space than other data structures.

A. GARCIA-ORTIZet al.

24

A Vertex Adjacency Matrix is a matrix representation which uses an m by m matrix A with its ‘th Y element aij set equal to 1 if vertices i and j are connected by an edge, and set to 0 otherwise. To represent a directed graph, m2 storage locations are required for a node adjacency matrix. For an undirected graph m(m - 1)/2, storage locations are necessary. The rather large storage space is a drawback for this data structure. Sedgewick [39] offers a C code listing for this data structure. A Vertex-Edge Incidence Matrix is a matrix N with m rows, one for each vertex, and n . .th element of N. Then nij is equal to 1 if edge j columns, one for each edge. Let nij denote the a~ is incident with vertex i, and equal to 0 otherwise. For a directed graph, we have nij set to +l if the ith vertex is the tail of the jth edge, set to - 1 if the vertex is the head of the edge, and set to 0 otherwise. The size of a vertex-edge incidence matrix is nm. If the graph is very dense, i.e., most pairs of nodes are connected by an arc, then the incidence matrix is much larger than the adjacency matrix. For sparse matrices, however, mn < m2, and it may be beneficial to use this representation. Shortest

Path Algorithms

[18-21,24,40]

From amongst all the classes of problems in network optimization, the shortest path problem (SPP) has been one of the most extensively studied. Suppose that we are given a graph G in which the edge (z, y) has an associated cost d(z, y). The SPP involves finding the path from a given starting node s to a destination node t, which has the lowest possible cost. Cost can be defined as the actual length of the edge, as the time-of-travel along the edge, or as the number of turns required to get from s to t. This type of problem commonly arises in vehicle routing and scheduling, communication, circuits, and production applications. Excellent reviews, bibliographies and surveys on the subject have been published by Deo [20], Dreyfus [21], Pallottino [24], Korf [27-331, Solomon [41], and Stewart [37]. Classification

of shortest-path

problems

In his survey paper, Dreyfus [Zl] classifies SPPs into five problem types: (1) (2) (3) (4) (5)

shortest path between a specified pair of vertices; shortest paths between all pairs of vertices in a network; second shortest path (Kth shortest path); time-dependent length-of-edges path; and shortest path which visits specified vertices.

The Kth Shortest Path may be beneficial in problems where some complex quantitative or qualitative feature characterizes the paths, and the shortest path possessing this additional attribute is sought. The time-dependent length-of-edges path applies, for example, when the time of travel between vertex i and vertex j depends on the time of departure from vertex i. The shortest path which visits specified vertices applies, for example, when it is desired to incorporate the driver’s travel preferences. Deo and Pang [20] give the classification of SPPs depicted in Figure 6. Under the Constrained Path class, we may include the driver’s preferences to visit specified intermediate vertices enroute to the destination, as well as specifying a certain number of edges that must be visited. Generalized Path Length refers to a more complicated function of edge lengths. Here we may include the types of roads and highways, as well as travel times and turn penalties at intersections. The two general algorithmic approaches used to solve SPPs are shown in Figure 7 [20]. Both approaches have polynomial time complexity. Label-setting techniques refer to algorithms such as Dijkstra’s, where the label is made permanent only when the path found is the shortest path from the source vertex to that vertex. In each iteration at least one vertex gets permanently labeled. These methods apply to networks with nonnegative edge lengths. Ford’s algorithm is a

Enabling

25

Technologies

SHORTEST PATH ALGORlTHh4s

USUAL

GENERALIZED LENGTH

PATH

PATH

/‘p---

_

I’

UNCtONSTRAINED

SHORTEST PATH

SINGLE SOURCE

PATH

K-TH SHORTEST PATH

CONSTRAINED

PATH HAS SPECIFIC NO. OF ARCS

DRIVER’S PREFERENCES

SUBOPTIMAL PATHS

PATH LENGTH AS FUNCTION ON PATHS

WITHTURN PENALTIES

PATH

-.

ALGEBRA RELATED PROBLEMS

-h-H CYCLES

ALL NODES AR!2 SOURCES

MULTIF’LE SOURCE

I-. \ j --_,

--,

ONE-TO-ONEONE-TO-ALL

ALL-TO-ONE

Figure

ALL-TO-ALL

6. Classification

of shortest-path

problems

generalization of Dijkstra’s algorithm to accommodate edges with negative lengths. Other labelcorrecting methods include the D’Esopo-Pape and the Threshold algorithms [18]. Another class of label-correcting algorithms, called Partitioning algorithms, was proposed in 1985 by Glover et al. [42-431. These algorithms have been shown to dominate other algorithms in terms of comprrtational performance, both by the original authors and in subsequent studies [44]. Still other label-setting methods include the Binary Heap Method and the Dial algorithms. Bertsekas [lS] addresses performance and implementation issues for these algorithms.

Combinatorial and Algebraic Techniques

M&ix Methods

Nehvork Trwwsal

/

Line: Programming Problems

Labeling methods (reaching algorithms)

I /

Label-Correcting

-1.. --- , Simultaneous Linear Equations

‘-1

Label-Setting

Figure

7. Combinatorial

and algebraic

techniques

for solving

the SPP

_

Matrix MuRiplication methods

A. GARCiA-ORTIZ et al.

26 Spanning

tree

algorithms

[18,19,39]

A graph may contain several different trees. Several algorithms can be used to construct trees, spanning trees in particular, with certain optimality properties. Here we discuss two relevant “classical” problems: the minimum weight spanning tree, and the maximum capacity route. Let each town in a region of interest correspond to a vertex in a graph, and each highway segment correspond to an edge joining two vertices. To each edge, associate a weight representing the time of travel, length of travel, or cost of travel. The SPP for this situation can be posed as one of constructing

a minimum-cost,

spanning tree for the corresponding graph. This is refered

to as the Minimum-Weight Spanning Tree. Suppose now that each edge in the graph has a capacity c which represents the maximum amount of traffic flow that can pass through the edge. The Maximum Capacity Route Problem is to find a route between any two specified vertices such that the smallest edge capacity on the route is the maximum among all the routes. This problem can be solved by finding the maximum-weight spanning tree. The solutions to these two problems, given below, involve the use of the spanning tree algorithm outlined next. This algorithm examines the edges of the graph in an arbitrary sequence, and decides whether each edge will be included in the spanning tree. Thus, each edge is labeled as either assigned to the spanning tree or excluded from it. When an edge is examined, the algorithm checks for cycles which contain the current edge and other edges already assigned to the tree. If such cycles are found, then the edge under consideration is excluded from the tree; otherwise, it is assigned to the tree. As the edges are assigned to the tree, they form one or more connected components. The vertices belonging to a single connected component are collected together into what the algorithm calls a bucket. An edge forms a cycle with the edges already assigned to the tree if both its endpoints are in the same bucket. A unique number is assigned to each bucket and hence to all the vertices in the bucket. To check whether both endpoints are in the same bucket, we need only compare the bucket number assigned to those vertices. Note that each time a step of the algorithm is performed, an edge is examined. Thus, the time-complexity of this algorithm is O(n), where n is the number of edges in the graph. This algorithm has the property that each edge is examined at most once. Such algorithms are called Greedy algorithms and they are extremely efficient computationally. MINIMUM SPANNING TREE ALGORITHM. This algorithm performs the above spanning tree algorithm steps examining the edges in order of nondecreasing weight, i.e., smallest weight first, largest weight last. If two or more edges have the same weight, then they are processed in an arbitrary fashion. The minimum-spanning tree problem can be solved in 0(n2) time. Prim’s algorithm has O(n2), and Kruskal’s algorithm uses priority queues and has complexity O(nlogn) [39]. MAXIMUM SPANNING TREE ALGORITHM. This is similar to the minimum spanning tree algorithm but it examines the edges in order of nonincreasing weight, i.e., largest weight first, smallest weight last. If two or more edges have the same weight, then they are processed in an arbitrary fashion. A search strategy which utilizes a priority queue to decide which node to take from the data waiting to be processed (fringe list) is called a priority-first algorithm. Priority search is the generalization of breadth-first and depth-first search, because these methods can be derived through an appropriate priority setting. The oldest algorithm for such problems dates to at least 1957. For minimum spanning trees, the algorithm is attributed to R. Prim, and for the shortest paths it is generally attributed to the Dutch mathematician E. Dijkstra. Heaps are used for large graphs to guarantee that sorting operations can be completed in O(log n) steps [39]. The basic idea behind Prim’s algorithm is to add the shortest edge that is incident to the existing tree. In the case of Kruskal’s algorithm, it is to add edges in increasing order of length, and reject any edge that completes a loop. The Greedy algorithm does the best thing at every step. Finally, for Dijkstra’s algorithm, the key idea is to build out from the source vertex s and

Enabling Technologies

27

-Spanning Step

1:

Tree Algorithm

Select any edge that is not a loop. Assign this edge to the tree and place both of its endpoints in an empty bucket.

Step 2:

Select any unexamined edge that is not a loop. If no such edge exists, then stop the algorithm as no spanning tree exists. Otherwise: one of four different situations must occur: (a) Both endpoints of this edge are in the same bucket. (b) One endpoint of this edge is in a bucket, and the other endpoint is not in any bucket. (c) Neither endpoint is in any bucket. (d) Each endpoint is in a different bucket. If condition (a) occurs, then exclude the edge from the tree and return to Step 2. If condition (b) occurs, assign the edge to the tree and place the unbucketed endpoint in the same bucket as the other endpoint. If condition (c) occurs, then assign the edge to the tree and place both endpoints in an empty bucket. If condition (d) occurs, then assign the edge to the tree and combine the contents of both buckets in one bucket, leaving the other bucket empty. Go to Step 3.

Step 3:

If all the vertices of the graph are in one bucket or if the number of assigned edges equals one less than the number of vertices, stop the algorithm since assigned edges form a spanning tree. Otherwise return to Step 2.

Note: Initially all edges are unexamined and all buckets are empty. -get to one permanently settled vertex at each step. For example, find the vertex nearest to $5,i.e., s itself with distance dl = 0. After k steps we know the distances dl, da,. . , dk to the kth nearest vertex. Let l,, denote the cost associated with the edge between vertices i and j. Suppose now that the vertices are numbered 1,2,. . . , k as they are found, with the source vertex s being vertex 1. Then the problem is to find the next nearest vertex that minimizes d, + l,j, where i represents a vertex that is already settled, and j is a new vertex in the fringe list. For a minimum spanning tree, it was only the cost lij that was minimized, and which gave the length of the new edge. Now because all distances are measured from s, the cost di from s to i is also included. The best path will jump in a single step from a settled vertex to a new vertex because if the vertex j VriLsseveral steps away, then the intermediate vertices would be closer to node s. Streamlined versions of the algorithm use an updating procedure to execute the algorithm. This reduces the count to O(n’). Heuristic

search

Heuristic solutions to SPPs are discussed by Stewart [35-381 and Korf [27-331. Other relevant discussions are given by Dechter [45-461, Kanal [26], Schildt [34], and Tanimoto [38]. The A* algorithm is perhaps the most widely implemented form of heuristic search. Its characteristic feat,ure is the evaluation function, f(i), which is the sum of two components: (1) the estimated minimum cost, g(i), of a path from the initial vertex s to the current vertex i m the Open list, and (2) the estimated cost from vertex i to the destination vertex t. The first component can be easily calculated if the search space is a tree, or it can be approximated by the shortest path if the search space is a graph. The second component must be defined in a way that it takes the problem domain features into consideration and the search proves to be admissible. The original work on A* was published in 1968 by Hart, Nilsson and Raphael [47],

28

A. GARCIA-ORTIZ et al.

with a correction published in 1972 [48]. More recent results, extension and implementations are discussed by Korf [27-331, and Stewart et al. [35-371. We also refer the reader to the papers in this special issue by Chang, and by Nelson, and to the references therein. Other

shortest-path

problems

In addition to the types of problems discussed earlier, we may encounter situations which require a shortest path from a single origin to multiple destinations as illustrated in Figure 8. This is refered to as the SO/MD

problem.

Here the traveler wishes to find the shortest route

which connects a given vertex s on a street network to highway entrances a through d. One way to accommodate

such problems is outlined by Bertsekas [18], who also touches on the multiple

origin, multiple destination problem (MO/MD).

Figure 8. Example of an SO/MD problem on a road network, i.e., how to go from s to a, b, C, and d.

Another option is to use Dijkstra’s Two-Tree Algorithm which grows a shortest-path tree from both the origin vertex s and the destination vertex t simultaneously. When both trees have a vertex in common and certain conditions are satisfied, the shortest path between s and t is said to have been found. This idea was first suggested by Dantzig in 1960 and further developed by Nicholson in 1966 [49]. From a computational point of view, Helgasson et al. [50] showed that the original Dijkstra’s algorithm generated a shortest-path tree containing approximately 50 percent of the original vertices until a path from s to t was found. The Two-Tree method was found to contain only about percent of the vertices. This can be of great benefit in algorithms that depend on repeated solutions of the SPP. Additionally, there are All Shortest-Paths algorithms by Floyd (1962) and Dantzig (1967) which have approximate complexities of O(2n3) and 0(1.5n3), respectively. Both require matrix computations. There are also survey articles by Karp (1975), Lawler (1971) and Knuth (1973) on efficient programming techniques applicable to such algorithms. The Kth SPP can also be solved by the Generalized Floyd and the Generalized Dantzig algorithms. Some improved algorithms for the Kth SPP are also given by Shier (1979). A computational complexity summary of several exact solution methods for SPP algorithms is given in Table 4. The number of vertices in the graph is n, and the number of edges is m. Evaluation

of SPP

algorithms

In our work on inter and intra-city travel we have implemented and tested several algorithms. Among them are: - Partitioning Shortest Path (PSP), - L2-Queue (L2Q),

Enabling Table 4. Computational . Dijkstra.‘s

SP Algorithm

. Partitioning

. L-2queue

(Pape-All-Dest) (Heap-All

1 . Heap-Se:-Dest

Algorithm

- Dijkstra’s

2-Tree

performance

path (DSP),

vertices,

A label-correcting

4n + 2m. A label correcting

m, equal to 28,702.

method.

method.

5n + 2m. Uses a binary heap. label +:ord

part of the path planning.

‘was a Sun workstation, Table 5. Execution

a Forward actually

For example,

to compute

the shortest

the algorithms

took.

of St. Louis City wit,h the computation

of

St. Louis

1.6 hours, and tht, L2Q

The host platform using MODSIM

used for the II

path problem algorithms

:.:eqprocrasing PSP preprocessing

times

data was the

For the complete

were implemented

data

Another

running

of the street

took about

times, in seconds, for various shortest

The

whereas

seconds.

path.

71, equal

Star.

for a portion

took 9 to 10 seconds,

edges, the preprocessing

of vertices,

used the same

took to complet,e.

the preprocessing

took an average of 0.107

and 28,702

took only 4.1 seconds

namely

data preprocessing

5. As seen in the table,

algorithm

All the programs

how long each algorithm

how long the street

evaluation

St.

A

and

edges, and distances,

the path using the L2Q algorithm

Subset

time.

O(n ); finds shortest distances between all pairs of vertices. Performed well for transportation problems: took ZO’?? to 35% less time than L-deoue or S-Dial.

and 996 edges, the preprocessing

City, with 9,105 vertices

O(n2).

running

for the city of St. Louis with the number

of edges,

are given in Table

most time consuming

0(n2)

(D2T).

was used to determine

379 vertices,

methods.

of S-Heap; single origin, multiple destination. i A modification 3 !

was used to determine

obtained

Storage

O(m logn); Storage method.

was evaluated

to represent

A timer timer

[l&22]

and the number

structures

with modification

O(n2m);

all pairs) [18,22]

shortest

for various exact solution

A bidirectional search; Dijkstra’s algorithm generated ab#out 50 percent of all vertices to find a SP from “s”to “t”; the two-tree method generated 6 percent of the vertices. Good for problems where repeated solutions of SPP are needed.

[1X:22]

- Dijkstra’s Their

[19]

[18,22]

Dest)

. PSA (primal sequential

to 9,105

O(nm),

[19,43,44]

Two-Tree

summary

Requires 3n(n - 1)/2 operations; label-setting method.

[51]

Algorithm

. Dijkstra.‘s

. S-Heap kkg

complexity

Technologies

PSP

DSP

DSP

I preprocessing

I

of

Louis City

n = 379, m = 996

9.781

0.109

9.806

1.047

9.817

0.179

9.818

0.107

9.802

1.002

9.790

0.179

9.923

0.107

9.806

0.978

9.782

0.179

Mean

0.1077

1.009

Std. Dev.

O.l15E-2

0.350-

St. Louis City n = 9105, m = 28702 Mean Std. Dev.

In this path

I-

planning

to path planning,

5784

section,

4.094

5774.8

582.04

0.179 1

0.3735E&8

13.56

5769.7

5776.2 7.237

we have presented

and have provided

have found that it is only meaningful

references to compare

a summary

of several

algorithms

to and surveys of such problems. only specific

implementations

applicable

In our work we of the algorithms

A. GARCIA-ORTIZet al.

30

rather than the algorithms themselves. Moreover, such comparisons are highly dependent on the data, and the data structures and compilers used. For an additional comparative study of algorithm performance, we refer interested readers to Bertsekas [18] w ho implemented various algorithms on a 16MHz Mac Plus using an Absoft compiler. The path planning problems vary in size from 1000 to 5000 vertices with 5000 to 25000 edges. For the SO/SD problem, the results of the Heap-All-Dest algorithm are compared with those of Pape-All-Dest algorithm. The running times are shown to be from about half a second to about 2.5 to 3.5 seconds. For the SO/MD problem, the results of Heap-Select-Dest are compared with Bertsekas’ Auction-Select-Dest

algorithm

algorithm; again the running times are shown

to be within fractions of a second. We have also given the time complexities of the algorithms when possible, and based on this we have recommended several algorithms for particular application. MO/MD shortest-path

It is our belief that SO/MD and

problems for a few thousand vertices can be solved on-line, and in-vehicle,

in a few seconds. For a higher number of vertices and edges it will be advantageous to consider heuristic algorithms and AI search methodologies, as well as segmentation of the street database. Relevant information can be found in references [51-671. From a research viewpoint, we find that an innovative approach to path planning would be to change the graph representation so that vertices represent streets and edges represent intersections; the weights assigned to the vertices represent traffic density and/or travel times, and left/right turn restrictions are incorporated by assigning directions to the edges. This approach was suggested to one of the authors, M. Amin, by Subhash Suri who has done work in this area [68]. Inclusion of time-dependent links and routing over stochastic and dynamic networks is also a much more realistic scenario, yet render the problem much more difficult. Other planning issues include multimodal routing and traffic mangagement, e.g., considering a two-level hierarchical network problem.

HUMAN

FACTORS

Introduction Another technological area that has widespread applicability to the field of ITS is human factors. This technology is charged with ensuring that engineered systems meet human cognitive and motor capabilities, and its application requires close work between engineers and psychologists. Its biggest technical growth stimulus occurred during World War II when military planners realized that success could be achieved only if an effective match between man and machine could be effected. One result from the time still in use today is the scale markings on fuel gauges [69]. Nowadays fuel gauges indicate relative fuel level, i.e., between “Full” and “Empty.” Prior to this labeling scheme, gauges indicated fuel supply in gallons; this led many a pilot who flew a mix of planes to miscalculate the available fuel supply for a mission, and ultimately resulted in the loss of life and equipment. Other developments of the time include the work of Ezra S. Kendrel who came up with a mathematical formula that described the hand-eye coordination ability of pilots. This formula led to improvements in airplane design efficiency and safety margins. A more recent example of human factors engineering relates to the design of rotary telephones. At one point in its life cycle, the rotary phone housing was made larger, and the numbers/letters placed on the outside of the dial, the objective being to make it easier for a person to dial a phone number. A study revealed that people actually had more trouble with the new design. The solution was to add white dots inside the finger holes; it turns out that people were using the numbers/letters in the old design as guides for placing their fingers. While these examples serve to put human factors engineering into perspective, they also serve as a reminder that as we move forward to actualize advances in transportation through ITS, we must pay due attention to the physical, mental, and psychological factors that characterize those who are to benefit from the new systems.

Enabling Technologies

The key application

to ITS is the simplification

to the design of man-machine

31

of cognitive tasks through careful attention

interfaces, and in particular displays. The definition of a display

used here is that given by Stokes, Wickens and Kite [70], namely, the “structured and purposive presentation of information to any of the five senses.” The use of artificial intelligence has been proposed by Malec and Morin [71] as a way to help ease the workload on the driver, but; that will not be covered here. Displays, if not properly designed, can adversely impact human performance in two ways: they may exceed an individual’s cognitive resources if too many parameters are presented simultaneously, and they may slow down performance if sufficient information is not provided in a timely manner.

In the first case, performance is said to be resource limited, and in the lat,ter case it

is said to be data limited. these conditions exist.

During the design of a display it is important

to determine if any of

In ITS two very distinct display design situations exist: (1) the design of in-vehicle systems with which a person interacts while driving a vehicle. and (2) the design of off-vehicles systems. In-Vehicle!

Systems

The design of in-vehicle display systems is very critical for public safety reasons.

You do not

want to field a unit that distracts the attention of the driver to such an extent that an incident may arise which may result in personal injuries, injuries to people in other vehicles, or injuries to pedestrians. The cognitive demands placed on the driver need to be understood. For this reason, methods to measure the safety implications of these information systems must be developed, and good design practices established. In the U.S., a comprehensive set of guidelines is being developed by the National Highway Traffic Safety Administration to address the human factor aspects of crash avoidance systems. The guidelines address both generic and specific requirements for blind spot, backup. driver alertness, and headway warning devices. Among the issues being addressed are: warning actions, device status and control, and compatibility with driver actions. The guidelines deal with the design of the driver interface: how information is displayed and how the device is operated. No equivalent document exists for vehicle navigation systems, but many of the guidelines are applicable. The document addresses four signal modalities: verbal, visual, acoustic, and tactile. In Europe, the HARDIE project was established under the European Community’s DRIVE II program to promote good design practices and standards for in-vehicle systems [72]. Its primary focus is on road and traffic information, navigation and routing, and collision avoidance. The project determined that no man-machine standards existed which could be applied directly to a vehicle. In order to come up with a standard, a high quality, man-machine interface with the capacity to present both verbal and visual data was developed. This interface is intended to serve as a reference against which existing and new in-vehicle. man-machine interfaces will be ra,ted. Two modes of attention allocation can be used for the presentation of information: serial and parallel. The serial mode, as the name implies, relies on the sequential processing of inform&ion. An example of this would be the visual scanning of the road and the instrument panel during driving to ensure proper vehicle control. In order to extract information, the operator fixates on a particular item for a period of time known as the dwell time. The parallel mode relies oil the simultaneous processing of information by one or more of the senses, or through “automatic processing” if some of the tasks to be perforrned are a familiar ones. Examples of parallel processing are the use of peripheral vision to obtain vehicle position cues, and shifting gears while driving. In order to exploit the parallel mode for the design of vehicle displays, it is essential that the channels of information used by the driver do not compete for the same cognitive resources. In this regard Wickens proposed in 1984 the attention model shown in Figure 9. This model uses auditory and visual modalities, and verbal and spatial codes to present verbal and spatial

A. GARCIA-ORTIZet al.

32

information.

Parallelism is achieved when operating in the shaded quadrants since no competition

exists for the verbal and spatial information processing channels; i.e., the greatest gain in parallel processing is achieved through the use of different modalities rather than different codes. Format Verbal

Auditory m

Spatial

and Pitch

Modality

Visual

Print

Analog Pictures

Figure 9. Sample displays, those along a diagonal do not compete for cognitive resources.

Auditory

displays

Auditory communication is considered by far to be the most effective way to provide guidance instructions to a driver, the reason for this being that the presentation of spatial information on a visual display adds to the cognitive load already imposed by the driving task on the visual channel. In general, visual artifacts such as arrows and maps should only be used to support the auditory information. Verwey and Janssen performed a field study which involved the presentation of route guidance instructions in both visual and voiced form. They found that the visual form, using arrows, resulted in twice as many errors as the voiced form. In a later study, Verwey [73] found that the advantage exhibited by the voiced approach was due primarily to the format in which the instructions were given. Another reason for using auditory displays, in addition to the intrinsic unburdening of the visual channel, is that auditory signals evoke a quick response from the individual. Hence, they are well suited for the transmission of alerts and warnings. In using auditory displays, care should be taken not to disrupt concentration through auditory clutter. One such case would be the presentation of complex information in the form of a spoken message. Because speech as a communication media is slow, it requires that the listener wait until the message is completed before the information can be comprehended. The time involved may well lead to an accident in the case of a collision warning message. Visual

displays

Despite the clear advantage of the verbal modality, two cases can made for the need to supplement verbal instructions with visual instructions: (1) in a time-critical situation, the driver may lose part of the verbal information; he/she can recover by resorting to the information provided on a visual display, and (2) in complex road situations, such as roundabouts, the required verbal information may be too complex to be given in an unambiguous manner, and the visual approach may be easier to comprehend. Parks and Burnett [74] have compared the presentation of navigation information in visual form with a combined visual/verbal form. Their study sought to determine: (1) if the use of the voice modality in addition to the visual modality eased the driving task, and

Enabling

(2) if the presentation

of advanced

Technologies

information

33

in a voiced form helped the driver plan his/her

next turn. The visual

information

was provided

by an arrow-only

display.

The number

of glances

by the

driver to the display was used as an evaluation criterion. This is a fairly standard metric whose use is based on the assumption that a well-designed display will require fewer glances. On the basis of this metric the study showed that better driver performance is achieved when the verbal modality about

is used with no advanced

turning

the use of voice the drivers

(1) the presentation a higher number

indicated

of advanced of glances,

(2) the use of the voice modality like control Three

action notification.

However, when asked their opinion

that:

information

in a visual form was useful even though

it involves

and would be annoying

during

regular

commuting

so they would

over its use.

types of visual

displays

are commonly

reported

in the literature:

(1) a digital street map showing streets in the neighborhood of the vehicle, (2) a digital street map showing only the route to be followed from the current to the next turn, and (3) a graphic arrow artifact showing the required direction of travel. A novel display format via a head-up display.

is the perspective

In the design of a visual to be taken. into account:

display

(1) drivers focus their attention (2) navigation is done in terms

guidance

for an in-vehicle

display

which lends itself to implementation

navigation

on when their next turn of landmarks.

vehicle location

system,

will occur,

two human

factors

need

and

This assertion is supported by the work of Obata, Daimon and Kawashima [75] who compared the performance of a driver assisted by a human navigator with that of a driver assisted by a vehicle navigation system. Daimon has also showed that the manner in which a driver plans his t,rip is driven to a large extent by the tools available, i.e., a paper map versus a navigation system. In his study, “expert” navigators were asked to give route instructions in two ways: (1) graphically, by drawing a map, and (2) verbmally, by writing down the directions. The findings indicated that it is easier for a person to give directions by sketching the route on a “map.” Another phase of this study looked at what information a driver needs while driving. It turns out that drivers plan their driving in terms of a sequence of subgoals, primarily turning intersections, and they use traffic signals as their primary navigation of landmark. This information was used to design three prototype navigation systems: (1) a system using a digital map of the area and an arrow placed at the vehicle’s position showing the direction of required travel, (2) a system showing a sketch of the path to the next turning point with an arrow indicating the turning direction, and (:3) and an arrow-only system showing the desired direction of travel. Of the three systems, the path sketch approach was found easier to use and resulted navigation errors; the most difficult to use was the arrow-only system.

in fewer

Burnett and Joyner have compared an electronic route guidance system with route directions provided by a passenger and those obtained from a paper map [76]. This is in a way similar to the work of Obata, Daimon and Kawashima. The digital map display used was a heading-up map which shows a vehicle icon at the center of the display, and rotates and translates the street map as the vehicle moves. This contrasts with a North-up map which rotates and translates a vehicle icon on a stationary street map. The performance measure was the number of glances

34

A. GARCIA-ORTIZ et al.

required to assimilate the visual information provided. The results indicated that the use of an electronic route guidance system resulted in more navigational errors than when using a map or verbal instructions by a passenger, and led to a higher number of glances. The navigation errors were attributed in part to the time lag associated with the display updates, and in part to the use of a heading-up display which occasionally confused the driver. As in the case of the TrafficMaster system described earlier, the test subjects perceived a reduction in workload when using the electronic system in spite of the number of glances required. From the display glance data reported by Burnett and Joyner, shown in Figures 10 and 11, it is hard to tell if the increase in the number of glances really impacted driver performance or if it simply reflects better use of the driver’s cognitive capability. Is the number of glances taken by the driver a meaningful metric ? Drivers seem to find sufficient time to look around while driving, which begs the question, how much distraction can a driver sustain before he/she impairs the driving task? The real issue from a display design perspective is how long does it take for the driver to interpret the information given in the display. Hence, a more appropriate metric would be a measure of the display information complexity.

lo

1 Electronic Cl Paper 0 Verbal

Figure 10. With a navigation system, the driver takes quicker glances at the road, but glances remain about the same elsewhere.

What type of visual display is most appropriate?

Spoerri [77] compared two different map

display formats: (1) a bird’s eye view, global coordinate system presentation, and (2) a driver centered, coordinate system presentation. He contends that driving instructions should not just communicate the next maneuver but also provide a preview of what will come next. After all, a driver likes to know if he/she should continue straight ahead for a while, or if a lane change is needed, or if he/she will have to make another turn right away. For many of these situations, a verbal modality does not suffice because of the time required to verbalize the instructions, so a visual display is required. Spoerri proposes the use of a perspective guidance display. This type of display uses a perspective rendering to communicate the succession of street segments to be followed. The information is presented in a manner compatible with the perspective cues used while driving a car. A comparison made by Spoerri of the perspective display format against a heading-up display format, in a simulated environment, showed the perspective display to perform better.

Enabling Technologies

60

r

Figure 11. With a navigation system, the driver cuts back on the time spent looking away from the road.

It is vehicle images for the

interesting to note that human factors studies addressing the design of visual displays for navigation systems tend to focus on displays that provide route guidance using graphical whose complexity require that they be captured in the fovea1 region of the eye in order driver to be able to process the information presented. Under these conditions, the arrow-

only display tends to yield the best results because of its simplicity. No use is being made of the parafoveal region of the eye which much like the verbal modality does not require the driver to take his/her eyes off the road in order to process situational cues. While human factors engineering does offer a complete methodology for the evaluation of invehicle systems, there exists a lack of correlation between laboratory results and field results. Underwood and Gehring [78] point to the fact that standard measures such as eye fixation time do not correlate with traffic accident data. Other

display

modalities

From a sensory perspective it is feasible to think of tactile, olfactory, and savory displays; however, irnplementation of these has not been pursued to the extent of auditory and visual displays. Operational

in-vehicle

systems

Several in-vehicle systems are already deployed throughout the world with Japan leading all ot,her countries in number of users. Some of the systems in use are described next. The DRIVE PLEIADES project uses a system built around the Milemaster, route planning product of the Automobile Association (UK) [79]. The road network coverage is limited to major roads and all principal junctions in urban areas. The Milemast&-% man-machine interface consists of a LCD mounted in close proximity to the speedometer, and a joystick used to initiate actions associated with “buttons” displayed on the LCD. The LCD is a 100mm diagonal, 640 by 480 pixels, color display. Origin and destination are specified by the repeated selection of letters from an alphabet grid until the desired location is matched. The output modality is text, graphics, and speech. Routing instructions are given either in text form or in map form. Because of the distance between the driver and the LCD, 700 to 800mm, the effective resolution of the display

A. GARCIA-ORTIZ et al

36

is 12 lines of text, 40 characters each, or 320 by 240 pixels in graphic mode, with two-pixel wide lines. This size of the text has been determined to be at the lower limit of acceptance by most drivers. However, increasing the text size would call for a larger display which would be difficult to install. Field evaluation of the unit determined that a mix of text and graphics illustrating the turn maneuver is difficult for the drivers to process, and that a more diagrammatic found to be desirable.

form was

The display also includes a bar whose color and length varies to indicate

the distance to the next turning point.

Speech is used to verbalize the text displayed on the

LCD. The TravTek program in the U.S. used a unit which provides visual and voice route guidance instructions.

Input is via a touch screen and visual output is via a color CRT. The first piece of

data provided by the driver is the trip destination. street name, or a prestored destination.

This could be an address, an intersection,

a

The type of data to be entered is selected from a menu

via the touch screen, and the data entry uses a virtual alphanumeric

keypad via touchscreen.

The next step is to specify the type of routing desired. The routing options are: fastest, avoid interstate highways, and avoid toll roads. Once the route is established it is highlighted on a map shown on the CRT. While the car is in PARK the user can zoom in and out; however, once the vehicle is in motion the map scale is set to 0.8 kilometers. Textual instruction is provided at the bottom of the map, as well as through voice messages. Once en-route, the display changes to a turn maneuver arrow display. As a safety feature, data entry can only be accomplished while the vehicle transmission is in the PARK position. Also, to reduce attention demands on the drivers, the system automatically switches to voice instructions and the turn movement arrow display. A study reported by Fleischman and Thelen [80] indicates that route guidance is valuable not only to out-of-town visitors but also to local drivers, even in the absence of traffic information. Both types of drivers tended to save destinations for later recall instead of re-entering the trip destination and routing preference every time; out-of-towners also made considerable use of the stored directory of services and attractions. The routing method of choice was the fastest, and the display of choice was the turn movement arrow. the authors believe that the latter may well be the case because this is the default display once the vehicle gets on the route. Finally, most of the time the voice guidance was left active by the drivers. A second generation system is being introduced in 1996 by General Motors as an option for their Oldsmobile 88. In this new configuration the CRT has been replaced by an active matrix, color LCD. The system is the same as the PathMaster system advertised by Rockwell. The target consumer price is about US$ZOOO. TrafficMaster is a congestion warning system operating in England which utilizes a radio pager network to notify subscribers on the traffic congestion conditions. This system is unique in the sense that it is both an in-vehicle system as well as an off-vehicle one. During operation the system displays traffic speed on a digital map of the road network. A study by Stevens and Martell [81] reported that use of TrafficMaster resulted in less driving related stress since the drivers could plan their trip ahead of time by using the system off-vehicle at their home or office. A user survey showed that 20 percent of the system users found the system distracting during driving, and 40 percent said that either the display was difficult to read or the audio warning was distracting. These results were attributed to (1) the placement of the display in the vehicle, and (2) unfamiliarity with the product features, i.e., users did not read the instruction handbook. The study also yielded an average time to read and interpret the display of between 4 and 5 seconds with several glances being required-on the average 2 to 4 glances, as many as 10 in some instances. The use of a flashing display was considered to be one of the sources of long glance duration. It is interesting to note that no significant increase in driving hazard was attributed to the traffic information unit by the drivers involved in the study. From a human factors design point of view, this study reveals that it is not enough to select a display modality; proper placement of the unit and an intuitive user interface are also critical design issues.

Enabling Technologies

37

Guidelines for in-vehicle systems design For traffic safety and driver performance considerations, it is important that a driver centered point of view be taken in the design of in-vehicle systems [82]. As the number of devices in the vehicle increases, so does the demand for driver attention. These devices can be categorized as follows: entertainment, vehicle and driver monitors, navigation, and crash avoidance. Careful attention must be paid to how each type of device call upon the driver’s attention to ensure that low priority issues do not mask high priority ones. An obvious change in the warning display should occur when a CAUTION warning changes to an IMMINENT

crash warning. The latter type of warning should be presented in at least two

modalities. This ensures sensory perception and system redundancy. Acoustic signals and voice messages in conjunction with a visual message are recommended. Cautionary warnings in general should be presented in visual form. The nature of the situation that led to the warning should automatically be provided to the driver. Multiple warnings should be prioritized in increasing order of urgency and severity. The warning should be terminated when the situation that led to it no longer exists. The warning should cause the driver to place his/her attention in the direction in which the danger exists, and should do so without startling the driver. Auditory displays include acoustic and speech displays. Used in conjunction with visual displays, they provide necessary redundancy. Audio should not be used for status display; they are recommended for imminent crash avoidance warnings. For cautionary warnings, visual warnings are preferred. The source of the audio warning should be consistent with the location of the hazard. This may be accomplished by using four speakers. Nondirectional warnings should be presented in the direction of travel. Acoustic displays should convey more urgency. Imminence should be indicated by a high signal repetition rate, high signal intensity, high fundamental signal frequency, or large signal frequency oscillations. On the other end, caution should be indicated using a low repetition rate, low intensity, low fundamental frequency, or small frequency oscillations. Signal intensity and duration coding is not recommended because people are poor judges of absolute levels of intensity and signal duration. Fundamental frequencies between 500 and 3000 Hz should be used. The particular frequencies selected should be those least masked by ambient noise. Pure tones tend to be annoying, while complex sounds tend to be easily identified. The intensity of the signal should be between 20 and 30dB above ambient noise. The onset of the signal should be between 1 and lOdB/sec so as not to startle the driver. The duration of the signal should be between 200 and 300msec. The warning should be repeated as long as the condition that led to it exists. In the U.S., the maximum intensity setting of the display should not exceed 115 dBA; at the minimum intensity setting, the warning should still be perceptible to the driver. Acoustic displays can convey time/distance information by increasing (1) the signal repetition rate, (2) the signal fundamental frequency, or (3) the signal intensity increases as the distance decreases. Speech displays should be highly intelligible and at the same time distinct from a human voice. They are suited for both cautionary and imminent warnings, but should not be used to convey distance/time information because of the time it takes to deliver a message. The message should be brief and concise, between 1 and 3 words, and it should be presented no more than three times to avoid irritating the driver or panicking the passengers. The speech rate should be about 156 words/min, not to exceed 200 words/min. This speech rate accommodates the memory performance of older adults. Visual displays provide the best means for presenting integrated information. This contrasts with auditory and tactile displays which are temporal in nature. Two types of displays can be identified: (1) primary displays which contain information that requires immediate at,tention, and (2) secondary displays where additional information is provided.

A. GARCIA-ORTIZet al.

38

Primary displays should be simple, able to catch the driver’s attention, and more conspicuous than secondary displays. Simple indicators should be used. Alphanumerics, graphs, and icons should be avoided. This allows the driver to quickly assimilate the information with a minimum number of glances. The display should be located within 15 degrees of the expected driver’s line-of-sight, and should not be obscured by other structures in the vehicle. At the minimum intensity setting the warning should remain visible. Secondary displays should not use flashing indicators as these might distract the driver’s attention from the primary display. The display characters should subtend at least 12 minutes of arc. All icons used should have an intuitive meaning. An imminent

crash should be indicated

by a rapidly flashing red indicator.

The flashing

rate should be between 3 and 5 flashes per second with a 50 percent duty factor. Cautionary warnings should use a continuous red display for the highest priority warning, and a continuous amber display for the second highest priority warning. When no critical situation exists, the display should be blank; green indicators should not be used since they may give the driver a false sense of security. Indicator lights should subtend at least a 1 degree visual angle. It should be pointed out that this color scheme is the scheme recommended for the U.S. Other countries may require a different color scheme as the use of color is highly dependent on the local culture. An example of this is the use of red signs in the U.S. to indicate an emergency exit; in Germany such signs are colored green. Status displays should provide an to indicate device status are: green red or amber when the device is ON avoided to avoid interference with a

indication of electrical power to the device. The colors used when the device is ON and has passed all diagnostic tests, but is not operating properly. Flashing indicators should be warning display.

Tactile displays are not well understood in an automotive environment and should be avoided. The controls (knobs) for different functions should be shaped differently. Size coding of controls should be limited to 3 sizes with diameters varying by 12.7 to 13mm, and thicknesses varying by 9.5 to 10 mm. Texture coding should be based on smooth, fluted, knurled, and serrated controls. Off-Vehicle

Systems

The human factors engineering aspects of off-vehicle systems are quite different from those of in-vehicle systems primarily because the users have plenty of time to digest the information being presented. These systems tend to use visual and verbal display modalities. Because they will serve a broader range of users from children to the elderly, and from the able to the handicapped, these systems face a different set of challenges. They will have to be easy to use, and readily accessible. Elderly

and handicapped

individuals

A fact not yet taken into account by any of the U.S. in-vehicle systems is that by the year 2030 the number of people 65 years and older is expected to double. This statistic points to a need for alleviating the natural deterioration in driving skills that accompanies aging. Suen and Parviainen [83] have proposed an Advanced Travel Accessibility System component to the set of services provided under the guise of Intelligent Transportation Systems. In Europe, under the DRIVE II program there are two projects addressing this area: TELAID (TELematic Applications for the Integration of Drivers with special needs) and EDDIT (Elderly and Disabled Drivers and Information Telematics). These projects aim at developing design guidelines and regulations for drivers with special needs and to examine the driving behavior of elderly people. In Canada, the work in this area has focused on trip planning and information systems that assist the elderly traveler. Research addressing the areas of human factors, traveler information, vehicle control, and decision making processes of the elderly and handicapped community is required.

Enabling

Technologies

39

Traffic management As ITS deployment gains impetus in the area of ATMS, there will be a clear need for human factors engineering in the design of traffic control centers. Present day traffic management systems such as those used by the Minnesota’s GuideStar and California’s Smart Corridor programs rely heavily on t.he use of television cameras to convey traffic “status” data to the center’s operator(s). The large number of visual displays, i.e., TV monitors, results in visual clutter, and the serial processing nature of the visual channel makes traffic management inherently slow.

The need

exists to develop multimodal displays for traffic management centers. Public

transportation

Not much is being done in regard to public transportation as evidenced by the small percentage of articles published. However, some activity does exist. For example, a portion of the EuroBus project has to do with the design of public transport, passenger information systems [84]. Their product is in the form of interactive street terminals and dial-up, desktop computer services that provide maps and timetables. The two major requirements placed on these systems are: (.l) adherence to the timetable, i.e., reliability, and (2) regularity of service. The Cassiope project has analyzed 119 public transportation systems in Europe. From their findings the list of passenger information functions listed in Table 6 was built. The project’s objective was to develop a meaningful user interface for public transportation. To that end a study was conducted to identify the manner in which people specify their trip requirements. A summary of the results obt.ained is given in the Figures 12-14. Table 6. Public transportation

time standard

for the system

map of the system showing routes standard

timetable

for each route

passenger

information

bus location

and time-of-arrival

bus destination

(on outside of bus)

bus route (on inside of bus)

fare information

connection

map of the area served by a route

service disruption

route change information trip planning intermodal

with user constraints

connection

information

information

desk connection

travel agent connection order of arrival at a stop

functions

information information

complaints/suggestions found/lost object

service service

local information entertainment

while waiting for bus

special services

information

help on using the travel help system

Traveler information Visual map displays are powerful because they can place considerable amount of information in a limited amount of space. One assumption rnade by designers is that the user will be readily able to translate the map’s plan view provided into a set of meaningful geographical features around him/her. This is not always the case. Hoffman and Stewart propose the use of text based routing in lieu of maps [85]. They have looked at two types of text based systems: ( 1) a Generated Text system that converts the map topology into routing instructions, and (2) a Stored Text system that makes use of landmarks that are readily obvious to the user, e.g., a pub. The Generated Text system requires minimal storage, but depends on knowing precisely where the vehicle is, and may give confusing instructions at a complex intersection.

40

A. GARCIA-ORTIZ

et al.

60

50

40 E i; 2

30

B s! 20

10

0

I

1

Trip Planning

Schedule

Other

Figure 12. Trip planning and schedule information are the top two services requested. 60

50

40 B i= ‘b 30 5 k! rZ 20

10

0

1

r

l-----l 1,

I

Origin

Bus Route

1

I

Time Window

I

I

Day of Trip

Figure 13. Trip destination and origin are the typical starting points provided for trip planning.

SENSORS Introduction Critical to the implementation of each technology area of ITS are sensors. ITS needs have profited and will continue to profit from the exploitation of prior developments in sensor and sensor related technology outside the domain-of transportation. Three key sensor technologies in particular will be exploited that owe their development to the defense industry, viz., GPS, image processing and MMW radar. Two of these will be given special attention at the end of this section. The field of sensors is so vast that a whole issue could be dedicated to any one area. Indeed, there are publications dedicated to such topics. It would be incorrect to attempt to duplicate such publications, and because of the need for brevity many sensors are omitted. In order to provide a framework to cover this vast field of sensors and applications, the authors have

Enabling Technologies

41

70

60

50 B s40 “0 5 $I! 30

20

10

0

_

I

I

stopPoint

Bus Route

Time Window

Day of Trip

Figure 14. The most sought after schedule information

is bus route information.

attempted to partition those sensor applications that relate to their use on board the vehicle for such items as driver assistance, vehicle health and safety to those sensors used outside the .vehicle for controlling and monitoring traffic flow, and measuring environmental conditions; see Table 7. Table 7. Sensor needs in intelligent transportation

Off-Vehicle Systems

In-Vehicle Systems Driver Health

Traffic Flow

Drowsiness Detection-Capillary Line of Fixation-Image

systems.

Pulse

Processing

Inductance

Loop Detectors

Magnetometers Microwave Ultrasound

Driver Assistance Ftoadsign Reading-Image

Processing

Driver Awareness

Video Image Processing Environmental

Conditions

Collision Avoidance

Road Surface-Capacitance,

Obstacle

Visibility

Avoidance

Temperature

Lateral Location Heaw Vehicle Navigation Water Temperature/Pressure

Goods

Weight-Fibers,

Load Cells

License Plate-Image

Processing

Oil Temperature/Pressure Fuel Level, etc. Emission Detection Acoustic

Automatic

Toll Collection

Smart Cards

Emission Detection Law Enforcement

Vehicle Security

Speed Sensors - RF Image Processing

Vehicle Location Smart Wheels

All the on board sensor needs relate generically to safety, navigation, vehicle securi-ty and vehicle health. In the area of driver’s health, research in Japan is on going to use capillary pulse sensors as an indication of the driver’s attention, and they are exploiting the driver’s line of fixation to not only sense the driver’s state but also to integrate it into other traffic awareness sensors as a safety measure.

A.

42

GARCIA-ORTIZ et al.

A special section is reserved later for image processing working with a TV camera or an IR camera, because that sensor technology offers the potential to be exploited in many areas both in on board vehicle and off vehicle applications. The line of fixation and direction the drivers are looking when they steer [86] exploit such image processing sensor technology. Similarly, in the area of drivers assistance with road sign reading, image processing is exploited, an example of which is described in Ritter as radar, in particular

et d’s

paper in this publication.

The same technology as well

MMW, and laser [87,88] can be used for drivers’ awareness in terms of

collision and obstacle avoidance [89], lateral 190,911 and longitudinal position information. The focus areas of low cost radar sensors are collision warning and intelligent cruise control. Radar technology applied to vehicles fall into three categories, (1) forward looking, (2) side looking, and (3) rear looking. The forward looking radars can be used for either intelligent or simple cruise control or as a safety device to warn of rear-end type crashes with the vehicle in front. The side looking radar is useful for forewarning of objects in the vehicles blind spot while lane changing. The rear looking radar is similarly useful for forewarning of obstacles in the path when the vehicle is in reverse. It is interesting to note that while there have been a number of systems developed in the U.S. for 10 GHz to 94 GHz, the U.S. systems are less mature than the Japanese and the European systems. This is because the FCC, to date, has not made a frequency allocation in the U.S., whereas the allocations in Japan and Europe are 60 Hz and 77 GHz, respectively [92]. As the Japanese and European radars are in the MMW region, it is reasonable to expect that the U.S. will also adopt this part of the spectrum, especially since this choice will result in compact solutions that can be mass produced. As the product liability concern is so high in the U.S., it is likely that the radar sensor will be applied first to the simple cruise control rather than the potentially most beneficial area of collision avoidance or warning. In the area of vehicle navigation, the biggest single impact sensor is the GPS system and the development of the inexpensive GPS receivers. Again, a special section is reserved for discussion of GPS as a sensor. Other developments such as triaxial accelerometers, fiber optic gyros, and silicon accelerometers are appended to that special section. In the area of vehicle health monitoring [93], there is a proliferation of sensors; however, the emphasis here is on collecting, collating and analyzing the data in real or near real time. The inclusion of a position locating sensor together with a communication system has enabled vehicles today to be secure from theft. And as many tire manufacturers include RF smart card technology embedded into the tires as an antitheft device, these costly vehicle components (especially for heavy goods vehicles) will become more secure. In addition, such technology is being used by several manufacturers, e.g., Epic Technologies Inc. [94], to constantly monitor tire pressure and will warn the driver when the pressure is too low. Any discussion of sensors used in vehicle applications would not be complete without reference to their growing utilisation for safety applications. Airbags have been used successfully in vehicles for a number of years; however, heretofore they have used single airbags triggered from multiple electromechanical sensors. The future shift is towards including side impact airbags as a supplement to the frontal airbags, and triggering these using single point electronic sensors. Siemens Automotive [94] have already introduced such a system which deploys lo-20 msec after the impact. Robert Bosch Corporation’s Automotive Group [94] has developed a sensor for the side operated airbag that triggers in under 5msec. Because of the concern over the safety of airbag inflation with a rear facing child seat, we will see additional sensors, e.g., capacitive, ultrasonic and infrared sensors, being used to detect the presence of front seat passengers or rear facing child seats in order to allow the airbag to be smarter in its operation. Having collision sensors, communication systems and on board navigation provides the basic sensor elements for automatic notification of an accident. Upon the detection of an accident and knowledge of the severity of the impact, an advisory could be automatically transmitted to advise the police, inform the traffic controller, inform the appopriate tow services and timely request for medical assistance. Such a system is being developed at Johns Hopkins University Applied Physics Laboratory [94].

Enabling Technologies

Finally,

in terms

of sensors

being applied

43

inside the vehicle,

no discussion

would be corrbplete

without reference to the work going on to enhance the driver’s vision. The use of electronic mirrors and video side mirrors do add to the driver’s visual perception of the road and the obstacles in the near

vicinity;

however,

the biggest

impact

to visual

enhancement

will be brought

the increased use of the night vision technology derived from their defense Such technologies as image intensification in the visible band and perhaps

about

by

sensor counterparts. the more promising

uncooled infrared sensors in both the 3-5 and 8--12 micron band have been tried experimentally wit,h promising results. The real driver for this technology is same affordability. As one progresses

to cover areas

of ITS technologies

outside

the vehicle,

sensor

technology

IS exploited principally to measure traffic flow [95]. To date, inductance loop detectors magnetometers have dominated this field. These sensors are inexpensive but costly to install

and and

replace. They are embedded in the asphalt of the road. Apart from the cost of the installation, thrre is an associated cost of the traffic delay while the device is being installed. As asphalt has a tendclncy

to move due to the thermal

extremes

of summer

and winter,

the sensors have a tendency

to break. Maintenance and repair is a correspondingly expensive exercise. There is a tendency toclay to move toward “noninvasive” types of sensors such as microwave radar, ultrasound and video image processing [96-981. To estimafe the condition of road surfaces, capacitance and thermal sensors are being embedded in the road. From the capacitance, a measure of the salt content on the road can be estimated leading to an estimate of the freezing point of the road surface. When this is coupled to the t,emperature measure, an estimate of the potential slippery nature of the road can be established. Another application of image processing can yield estimates of visibility. Visibility and condition of t,hQ road surface will provide warning of hazardous driving conditions. In the area of aiding commercial vehicles, experiments are on-going to measure their weight while the vehicle is in motion, so-called WIM or weight in motion. The success of such experiments will reduce the number of hours such commercial vehicles waste standing in line at weigh stations a~ well as the fuel they waste, and the pollution they generate. Such “weight” sensors exploit either piezoelectric, bending plate technology, or fiber optic principles. As an adjunct, automatic license plate recognition or automatic vehicle identificaimage processing will tion (AVI) and automatic vehicle classification (AVC) a g am . exploiting reduce the need for weigh station delays. The same technology is being used in the U.K. as a form of trafic regulation. License plates are recognized and correlated at a given spatial separatiou. By mc-basuring the time for the vehicle to traverse that separation, an immediate indication is :LcIlieved of exceedance of the speed limit. In deference to the RF or radar counterpart,, this system automatically yields the culprit. Again it is another example of exploitation of computer vihioll or image procession as a sensor for ITS applications. L’he final sensor which will be addressed in this section is the RF smart card. RF smart card applic*a.:-,ions are growing almost daily in all areas of our life. These cards are either active or passive ac:,ul,ding to whether they carry their own power source or whether they are powered by rectifying th(l RF from the interrogation source. They consist of a small antenna, an electronic integrated circuit (normally CMOS) and a battery (for active cards). They are physically small of the order of a c,redit card or smaller, and have a normal range of around a few meters. Information can be transferred to the interrogator remotely and at high speed. In large quantities, these electronic R.I” cards are inexpensive. We will see them used for automatic toll collection again obviating thr, need for toll booths which slow traffic causing loss of time, fuel and increase in pollution levels. This sensor technology is the parallel of the technology embedded by tire manufacturers described earlier, and is obviously a candidate technology for automatic vehicle identification. GPS

and Other

Vehicle

Navigation

Enabling

Sensors

Perhaps one of the most enabling sensor technologies that is applicable to many thtx ITS program is the Global Positioning System (GPS). GPS was a US$lO billion

facets of program

A. GARCIA-ORTIZet al.

44

pioneered by the Department of Defense to provide worldwide position, velocity and time data to military forces around the globe. GPS was envisioned, from the very first, to be a dual use technology and is regarded as the epitome of that model. It has been so successful a program that it has changed the way we navigate and determine location forever. GPS consists of a constellation of 21 satellites with three in orbit spares deployed in a 10,000 Nmi circular orbit with a I2 hr period. Four satellites are located in each of six planes inclined at 55’ to the plane of the earth’s equator. Each satellite continuously broadcasts pseudorandom codes at two frequencies Ll at 1,575.42 MHz and L2 at 1,227.6MHz. Ll is modulated with two types of code, the course acquisition code (C/A) and the precision code (P). L2 carries only the P-code. By knowing where each satellite is at any given instant, the GPS receiver can determine the distance to each satellite, and through simple coordinate geometry it can determine its own terrestrial location.

At least

three satellites must be visible to the receiver to obtain latitude and longitude; to obtain elevation a fourth satellite is required. Prior to GPS, on board vehicle position location and navigation were achieved through the use of inertial navigation sensors, usually a triad of orthogonal gyrometers and accelerometers. Whereas individual sensors have improved in accuracy, reduced in cost and increased in reliability, and whereas increased component dynamic range has allowed strapped down systems to be implemented, there has been no real breakthrough in the industry since the first inertial measurement units. Today a whole revolution has taken place. The United States GPS Industry Council (USGIC) worldwide GPS sales “totaled $420 million for 1993 and should top $600 million in 1994” [99]. The applications for GPS are multiplying at an enormous rate. Small in size (hand held) and relatively inexpensive (less than US$500) GPS receivers are now available off the shelf by any number of manufacturers [loo]. The impact of GPS on navigation systems throughout the world is marked. Krakiwsky [loll notes that of all the techniques used in 136 navigation systems identified between 1975 and 1993, GPS is the first with 56%, dead reckoning is second with 49%, then comes map matching with 25%, terrestrial 19%, sign post 15% and inertial 2%. It should be noted that some systems use multiple positioning sensors. Other studies [102-1041 compare GPS and differential GPS to radio location resembling LORAN or inverse LORAN, radio positioning, cellular phone tracking, and electronic benchmarks or sign posts in the roads. Although GPS seems to be the front runner, there are areas, particularly in built up urban districts or in underpasses, where the line of sight to the satellites is blocked. In these areas, a backup system is required. In that case, the availability of inexpensive inertial sensors is a must. The development of relatively inexpensive gyros of the vibrating wire, the MHD, and the more modern fiber optic gyro all fall into this category and owe the stimulus of their development in the U.S. to defense applications. The development of bulk and surface silicon micro machining [105] which yields inexpensive solid state accelerometers, and quartz flexured force rebalanced accelerometers [106] will facilitate inexpensive backup systems to GPS. All have been used by the military for a number of years. Image

Processing

As GPS had its origins in the defense sector and rapidly transitioned to the vehicle navigation sector, so too has image processing, or computer vision, transitioned from its defense department origins to find applications in traffic monitoring and other areas of future transportation systems. This is not meant to suggest that the objective of finding and automatically recognizing tanks and helicopters is identical to counting and quantifying by class traffic, nor would the authors pass any judgement as to the comparison of the relative ease or difficulty of either task. The technology of image processing is common to both, and the huge legacy of existing technology [107-113) already developed for defense applications can be leveraged for the tasks facing the transportation industry. As explained earlier, the advantage of using a camera/image processing combination to monitor traffic is that it is relatively easy to install, inexpensive and reliable. Its passive nature is a plus

Enabling

when compared a burst

to its RF counterparts.

of RF energy

45

Technologies

The social impact

in order to measure

of giving every driver and passenger

traffic flow has not yet been put to any public test of

acceptance. As the processing ity of cameras, expanded scientific

of cameras reasons

to handle

for looking

an exposition

growing

number

camera

towards

image processing

camera adjunct

of vehicles

weather. however,

Agam

there

of these arguments.

sensors

monitoring,

There of space

of references

cameras

that

does not

[114115]

is that

are becoming

more and more competitive. Whereas

are some notable

are a

cornmer-

Whethfar

most applications applications

which

there

there is always the need for the comput,cr

there

and

are sound

in the mid to far IR band

is of significance

IR focal plane array (IRFPA) or an IR camera,

cameras.

the same constraint

What

and affordabil-

will be incrementally

on the growing capability CCD

are a number

to make the task autonomous.

been in the area of traffic guidance

the longer wavelength

inclement

sensitivity

this technology

band especially

and the price is becoming

[116,117]

is a. visible

and the resolution, improve,

here to write an exposition

on the subject;

of uncooled

cameras

in the visible

with the essence

available

increases,

(IR)

operating

t,he case of highly

deal adequately cially

infrared

in use. It is not appropriate

availability

permit

power of computers

especially

the

vision or

to date have

to the autonomous

[118,119].

COMMUNICATIONS Introduct

ion

Communication is the “glue” that will hold ITS together. locations

established,

interested

parties,

At opposite inating

and road conditions

detected,

then all the sensing and data gathering

ends of the communication-alternatives

the data, the sensors

(2)

the data can be collected

can transmit

disseminated

t,o

effort is of no value.

spectrum,

we find two options

for dissem-

the data collected at some central

directly

to the end-user,

location,

and

where it may be processed.

an6 then

to the end-user.

Which

approach

tecture

Development

will eventually Program

be used is one of the issues being addressed in the U.S.

and dissemination,

transmitted.

in which data is currently

mechanism:s

used here include copper wires, fiber optics,

exchanged

and cost hurdles

Archi-

that

there

is the all important

issue

To find an answer we need look no further

the manner

limitations

by the ITS

[120,1.21].

to the issue of data collection

of how the data will be physically

bandwidth

vehicle

be disseminated

namely:

(1)

In addition

Traffic counts may be taken,

but if this data cannot

between

digital

computers.

and radio waves.

will dictate

than

The transmission

Each one of these has

where in the overall

architecture

they

will be mosit effective. A considerable taken

number

in the U.S.

headquartered national existing

and abroad.

in Paris,

standards

France

ITS

very high”

Architecture

In September IVHS

Architecture

Aircraft,

America,

Co-operation Quoting

Asia and Australia

informa.tics,

in ITS

for the purpose

systems.

beyond each continent.

have been underand Development

of establishing

from their

report,

are not really aiming

The need for a truly international

and especially

road-vehicle

inter“The at; such oper-

communication,

[122],

Development

of 1993, the U.S. Development

Loral - IBM,

for Economic

communication

ation in the area of road transportation is therefore

to communications

keeps track of these efforts

in Europe,

and harmonisation

pertaining

The Organisation

for road-vehicle

RI‘1 programmes

coordinatio8n

of field trials

Rockwell

in the U.S. Department Program.

of Transportation

Four contracts

International,

(DOT)

initiated

were awarded to teams

and Westinghouse

Electric.

the National led by Hughes

At the conclusion

of

A. GARCIA-ORTIZet al.

46

the program, in mid-1996, a national ITS architecture

will have emerged. The architecture

will

describe how the various ITS components interact to achieve the overall system goals. It will also define the system operation, the system components, and the data exchanged between them. By April of 1994 several architectural concepts had emerged from the program [120]. The Hughes team concept focused on the integration of the vehicle and the road. The vehicle provided the road with real-time, local traffic data; the road provided the vehicle with system-wide data, among them safety warnings, travel advisories, and route directions. decentralized

The system would be

to achieve maximum reliability at the lowest possible cost. Wide-area communica-

tion channels from the “information super-highway” would be used to transmit

live video, and

traffic, road and weather data from road-side locations to the traffic management center (TMC). Vehicle-road

communication

would be similar to that now used for electronic toll collection.

In

addition, real-time traffic information and transit schedules would be disseminated to the general public via cellular phone and cable TV. The Loral-IBM

ITS concept was built around the networking of existing transportation

related

entities, such as the public transportation system and the highway patrol, using existing communication capabilities. The communication standards to be used would be those currently in use by computer and communication networks. The Rockwell architecture offered a modular architecture based on the user services to be provided. The communication framework would accommodate both wide-area and short-range data transfer. Wide-area communication includes both wireline and wireless two-way communication technology. Short-range communication would be wireless. A simple broadcast capability would be used for one-way dissemination of highway traffic advisories. These public service broadcasts would be via radio, TV, and highway advisory radio (HAR). Variable message signs (VMS) would inform drivers of alternate routes. Telephone, cellular and personal communication services (PCS), as well as computer networks, would be used to disseminate travel information. Westinghouse’s concept was based on maximizing the people-carrying capacity of the existing road infrastructure, the cost effective management of traffic and incidents, the use of cellular and PCS devices for MAYDAY notification, and minimizing in-vehicle equipment through the use of FM subcarrier and digital audio broadcast (DAB) services to convey travel information. By November of 1994 these various ITS architectural

concepts were beginning to converge

as a result of the public opinions expressed in a series of public forums sponsored by the U.S. DOT [121], especially in the area of communications. Fixed-location elements within the architecture, such as TMCs, would use land lines and satellite communication. The land lines include telephone and fiber optic cables. Mobile elements, i.e., vehicles, would use a wide range of options that allow l-way and 2-way communications. Wireless broadcasts would provide a low cost means for the uniform distribution of limited traffic data over a wide area. Cell-based communication would offer l-way and 2-way information exchange. Beacons, which are short range communication devices, would offer l-way and 2-way communication between the vehicles and the road infrastructure to convey position or location specific data. These beacons would be based on the use of infrared, millimeter wave, and microwave communication technologies. Land-Based

Communications

For most of its history, telecommunications has been dominated by analog transmission. Modems made it possible to transmit digital data at the relatively low speeds of 1,200, 9,600, and 19,600 bps. Data compression schemes now allow transmissions at data rates of 14,400 and 28,800 bps over the same physical lines. These rates, however, are insufficient for the high speed communication links required for computer-to-computer data exchange, and video transmission. For example, a single video frame captured as a 512 pixels by 512 pixels, 8 bits per pixel, represents 2,097,152 bytes of data. At 30 frames per second the transmission of video requires a media capable of supporting 62,914,560 bps.

47

Enabling Technologies

To address the need for faster data transmission, were established

in the U.S. during the 1960-70

a variety of digital communication

services

time frame. The various levels of digital carrier

rates are shown in Table 8. The Tl service was designed to operate over twisted wire pair cables, the T2 service came into use in 1972 to operate over wire pair trunks. Fiber optic networks have also been installed in the U.S. at three carrier rate levels: 274 Mbps (T4),

565 Mbps, and

1.2 Gbps. Table 8. U.S. telephone digital carrier rates. Carrier Name

Rate [Mbps]

Voice Channels

Tl

1.544

24

TlC

3.>!

.48

T2

6.312

96

T3

44.736

672

T4

274.176

4032

Responding to similar needs in Europe, the CEPT recommended the family of digital carrier rates shown in Table 9. In addition, three levels of fiber optic networks have been implemented. The data rates for these are: 32 Mbps, 140 Mbps, and 565 Mbps. The Japanese digital system is described in Table 10. Table 9. CEPT digital carrier rates. Level

Rate [Mbps]

Voice Channels

1

2.048

30

2

8.448

120

3

34.304

480

4

139.264

1920

5

565.148

7680

Table 10. Japanese digital carrier rates Level

Rate [Mbps]

Voice Channels

1

1.544

24

2

6.312

96

3

32.064

480

4

97.728

14&J

5

397.200

5670

For applications that involve data exchange between geographically fixed sites such as between a TMC and a suite of vehicle sensors deployed along an interstate highway, the use of land lines is probably the fastest and most cost effective for an urban area. In a rural setting where telephone lines do not; exist, the use of line-of-sight, spread spectrum radio appears to be the solution. Wireless

Communication

Wireless communication can be categorized by the type of network used [123]. Local area networks (LAN) permit the user to communicate with other users m an office or “work area.” Metropolitan area networks (MAN) extend the service range to a city and its surroundings. Finally, wide area networks (WAN) offer a national and international communication capability. Since LANs require short distance wireless links they are typically based on the use of infrared and spread spectrum transceivers which have a range of several hundred feet. MANS provide

A.

48

Table 11. Technologies

GARCiA-OKrIz et al. in use for Metropolitan

Area Networks,

Technology Packet Radio

Packet Cellular

a-way

P-way

data

data

voice/data

o-8 Kb

not available

>8Kb

large metro areas

not avaijable

2.4-4.8 Kbps

19.2 Kbps

2.4-4.8 Kbps

2.4 Kbps

data sent

data sent

connect time

no. of messages

Type Application Optimal Coverage Speed

message

Charges

Circuit-Switched

Cellular

Paging

a-way

l-way data +240

metro areas

bytes

metro areas

l-way and 2-way communication through the use of paging, and circuit and packet switching. Table 11 shows the various alternatives available for this type of network. Because of their coverage, WANs are usually based on the use of satellites. One typical application is in fleet management where communication may be required in remote areas where radio signals cannot reach. Satellite communication services throughout the world range from the Inmarsat (U.K.), to the MOBILESAT (Australia) [124], to the EUTELSAT (Europe), to the GEOSTAR (U.S.). Inmarsat provides data transmission at 9,600 bps in the Inmarsat-B system, and 2,400 bps in the Inmarsat-M system [125]. The MOBILESAT system offers data transmission at 4,800 bps. Several networks using low Earth orbit satellites (LEOS) have been proposed or are under development. These networks are expensive and thus tend to serve niche markets. Two types of mechanisms are in use for delivering data over a network, be it land-based or wireless. They are circuit switching and packet switching; see Figure 15. Circuit switching is exemplarized by the common telephone call, i.e., a connection is established first, and then data is transmitted over the connection. This is referred to as a connection mode of operation. In this mode the network establishes a session, monitors its progress, and when completed it terminates the call. Circuit Switching

Packet Switching

data

-

Figure 15. A connection oriented (circuit switching) network needs an end-to-end connection before data is sent; a connectionless oriented (packet switching) network sends data packets from node to node until they reach their destination.

Packet switching, on the other hand, can be compared to sending a letter by mail, a page at a time. The letter is first separated into individual pages at the origin, mailed, and then

Enabling Technologies reassembled

at the

destination.

49

In the terminology of networks, a message is broken into data

packets, the packets are then sent across the network perhaps following different paths depending on the link congestion, and finally the packets are collected and reassembled. This is referred to as a connectionless

mode of operation.

In this situation the network is also responsible for

addressing the packets and packet flow control. Communications

in ITS

One of the earliest examples of communications in the ITS arena was the Auto-Notfunk emergency call system. The system used radio triangulation to locate a disabled vehicle; an emergency assistance vehicle would then be dispatched to the site. This technology is now widely offered in the U.S. by companies like Pat-Tel. In genera& communications for ITS can be classified into three broad categories: local, regional and wide-area. The media used may be land-based or wireless. In most cases it is a combination of both technologies. communication

Table 12 summarizes several international programs and the particular

approach used [122].

Table 12. Some international use.

ITS programs and the communication

Regional

Local Loop

Program name

Infrared

category

Microwave

Radio

Cellular

they

Wide-area RDS

Satellite

.

RACS AMTICS LISB/.4LI-SCOUT

.

TRIADS

.

TRAFFICMASTER ADVANTAGE

I-75

HELP/CRESCENT

. . .

.

TRAVTEK ADVANCE FAST-TRAC

. . .

.

At the local level the communication technologies found are: inductive loops, infrared (IR) beacons, microwave beams, and radio frequency transmission. Inductive loops provide a coupling between the underside of a vehicle and the road surface. These systems operate typically in the range of 50 to 150 kHz, using a two-way full duplex communication link. The amount of data that can be transmitted is typically small due to the short communication range of the system and the vehicle speed. Infrared beacons can be used to transmit data to one or more vehicles within a 180 meter range of the beacon. An example of the use of such systems is the ALI-SCOUT system being used by the ADVANCE project in Chicago to communicate travel time data and determine vehicle position. Microwave beams are similar to the infrared beacons; however signal interference keeps them from being used in a broadcast mode. Their primary application has been for vehicleto-vehicle communication within a vehicle platoon. Radio frequency communication usually takes place at MF and UHF frequencies. Because of radio wave propagation characteristics, these systems are normally used in broadcast mode only. An example of this type of communication is the HAR used by many cities to convey traffic information to the driver via the in-vehicle radio unit. Regional. communication coverage ranges from several hundred feet to several miles, and operate at VHF and UHF frequencies. Examples of these are the pager and cellular networks. Route guidance systems such as SOCRATES use cellular radio data transmission. Wide-area communication services distances in excess of ten miles from the transmitter, typically in a broadcast mode of operation. One example of this type of communication is the

A. GARCIA-ORTIZet al.

50

radio data system (RDS) which uses an FM subcarrier frequency to transmit data to the vehicle. The information sent may be traffic updates similar to those provided by a HAR system, or GPS corrections communication

for use by a differential GPS (DGPS),

vehicle navigation system.

Satellite

is the ultimate example of wide-area communication.

When the ITS application involves collecting data from or delivering data to a vehicle, the obvious choice is wireless communications. In the ADVANCE project, IR beacons are used to collect local transit time data, and deliver system-wide, transit time data to several thousand probe vehicles. The former is used to update the traffic network model of the suburban area covered by the project, and the latter is used for in-vehicle, route planning. Another application of wireless communication is in toll roads where vehicles equipped with smart cards are debited on-the-fly as they drive past the toll booth. It is interesting to note that the same technology is being considered by the California

Air Resources

Board as a means of monitoring

vehicle

emissions [1261. Fleet management is perhaps the most wide-spread use of wireless communication in the ITS arena. QUALCOMM, with a customer base of about 40,000 vehicles for its OmniTRACS system, uses two satellites to determine vehicle location and to provide two-way communication between the driver and the dispatcher [127]. Data rates from the satellite to the mobile terminal set vary from 5,000 to 15,000 bps; from the mobile terminal set to the satellite data, rates range from 55 to 165 bps. Other companies such as Electronics & Space Corp. and Rockwell International, use ground-based, wireless, digital networks to relay GPS position data collected by the vehicle back to the dispatcher, and to provide two-way communication. The two primary public, wireless, digital communication networks in the U.S. are RAM Mobile Data and ARDIS. The former is a joint venture between RAM Communications and Bell South; the latter is a joint venture between Motorola and IBM, formed in 1990. ARDIS provides service to 400+ metropolitan areas in the U.S., covering 80 percent of the population, and 90 percent of the business centers. RAM boasts a coverage of 7,700+ cities in 12 countries around the world. Hybrid

Communications

The recent trend in communication is to mix land-based and wireless communication technology so as to provide the user with both mobility and high data transfer rates. The result is colloquially known as wireless digital networks. These networks operate in the 900MHz region of the spectrum. However, they have special attributes of their own. To ensure proper transmission of the data to and from a vehicle that is constantly moving from cell site to cell site, the network requires the use of protocols which break large amounts of data into data packets. The data packets are reassembled at the receiving end. The RAM Mobile Data network use the MOBITEX system developed by Ericsson, and the ARDIS network uses a system developed by Motorola. The MOBITEX system is a public, trunked, land-based, mobile radio system originally developed as part of a mobile alarm system for the Swedish Telecom Radio field personnel 11281. The network was first placed into operation in Sweden in 1986, and since then similar systems have been built in Norway, Finland, the Netherlands, the United Kingdom, the United States, and Canada; Australia and France have systems under development. The network is a packet switching or connectionless network consisting of radio base stations, local and regional switches, and a network control center. The radio channels are shared by all the network users. Because the data traffic passes through a myriad of communication components, the network adheres to the OS1 (Open Systems Interconnection) seven-layer model. The OS1 model is a set of communication standards and protocols aimed at establishing an open environment for the movement of data between devices. Open communication is where any device can communicate meaningfully with any other device regardless of the manufacturer and the proprietary protocols or formats they may use. The model consists of seven layers [129], each

Enabling Technologies

51

of which is architecturally independent. These layers are described in Table 13. The first four provide telecommunication functions on a node-to-node basis, the last three address the manmachine interface. The various protocols used by MOBITEX

in each one of the OS1 layers are

graphically shown in Figure 16. In the U.S., the data capacity of the RAM MOBITEX

network

is in excess of 252,000 data packets per hour at each node, and 2,800 to 13,000 data packets per hour in each radio channel. Table 13. Open systems interconnect

I-Layer

model

Purpose

Name

7

Application

Common communication

6

Presentation

Code conversion for common presentation of data.

applications.

Dialog control between applications.

5

Session

4

Transport

3

Network

Safe data transmission

in a network.

2

Data link

Safe data transmission

in a link.

1

Physical

Optimization

Transmission

of network resources.

of data bits.

Layer

Layer

Radio Modem

Mobile Terminal -

Base Station

w-u

Local/Regional

4--w

Switch

Host Computer

w

Legend MPAK - MOBITEX

Packet

MASC - M OBITEX

Asynchronous

M-232

- Serial Communication

Communication

Protocol

Interface

ROSI - Radio Signal Protocol GMSK - Giaussian Minimum Shift Keying 1II)LC - Master/Slave, X. 21 - Synchronous

Half-Duplex

Communication

.X.25 - Packet Protocol

Synchronous

Interface

Interface for V-Series Modems

for Full Duplex, Synchronous

Communication

Figure 16. The OS1 model and the MOBITEX

system

Charges for use of a public, digital network are based primarily on the number of bytes transmitted; an activation fee and a monthly service fee are also charged. For example, RAM’s activation fee 11sUS$50, its monthly fee is US$25, and data charges range from US$O.O4 to US$O.125 for messages between 2 and 512 bytes long. The cost of a vehicle transceiver is about US$SOO. Both Ericsson and Motorola manufacture wireless modems for the RAM network; Motorola offers products for the ARDIS network. Communication between the vehicle and the network ty.pically occllrs at a rate of 8000 bps, between ground stations the data transmission is via land lines. For applications involving a small number of vehicles and transmission of less than 512 bytes of data at a time, the Ericsson MlOQO, MZOQO,and M2190 modems support the popular Hayes AT-command set used by most telephone modems. There a:re two types of market competitors which exist for the wireless, digital networ.ks just described: cellular telephone networks and pager networks. The cellular telephone is perhaps the

A. GARCIA-ORTIZet al.

52

largest single contributor to the maturity and operational appeal of mobile communications. It brought with it point-to-point communication over the air with a certain degree of information security. The prior dominant technology, citizens band (CB) radio, offered one-to-many communication; i.e., anyone equipped with a CB radio could listen to a transmission. The expectation in 1982 for the number of cellular telephone users in the U.S. by the year 2000 was 1 million; almost 24 million had signed up by 1994 [130]. Cellular networks provide 2-way communication by using two frequencies, one for transmission and one for reception. In the U.S., the transmit frequency is between 870 and 890 MHz, and the receive frequency is between 825 and 845 MHz. The use of cellular telephone networks has two drawbacks: (1) they are primarily analog system designed for low bandwidth, voice communication,

and

hence, not the best for the transmission of high bandwidth, digital data, and (2) network use is charged by the minute, in addition to an activation fee, and a monthly service charge. In response to these limitations,

service providers are developing the cellular digital packet

data (CDPD) service. CDPD builds upon the existing cellular infrastructure, and it will operate much in the same manner as the RAM and ARDIS networks. Data rates at up to 19,200 bps are anticipated [131]. However, because all cellular telephone service providers will not concurrently adopt CDPD, and coordination of usage charges for an interstate, roaming vehicle will not be resolved for some time, use of a CDPD network will be viable only for local fleet management applications. Charges for CDPD service vary. In San Francisco, GTE has an activation fee of US$45, a service fee of US$15 per month, and a data fee of US$O.14 per kilobyte; Bell Atlantic’s charges are US$50 for activation, and US$O.17 to US$O.58 per kilobyte trasmitted. Discounted fees apply during off-peak hours. In contrast to cellular networks are the pager networks, such as PacTel, which are digital messaging networks. According to the Electronics Industries Association, pager sales in the U.S. went up close to 100 percent between 1990 and 1994, to US$230 million [130]. Over 30 million pagers are expected to be in operation by 1997. The low operational cost compared with cellular telephone service is the fuel that has fed the growth of this industry. These networks have considerable national coverage, they are digital networks, and service charges are well defined. In fact, PacTel TeleTrac is a vehicle location system that uses the PacTel pager network to establish vehicle location, and to provide one-way communication in case of an emergency. This service is currently available in 6 U.S. cities, with service planned for another 140t cities by 1996. In spite of their user mobility and high data rate capabilities, one common limitation of wireless digital communication networks is geographical coverage. System availability is usuahy limited to major urban areas and inter-city transportation corridors. For most ITS applications this is more than adequate. But, what about remote areas such as northern Canada? Well, for such situations the satellite-based, mobile communications mentioned earlier are in the horizon. Companies like Motorola, TRW, and Loral are spending some US$20 billion to put in place LEOS constellations. Still, as pointed out earlier, the operational cost of satellite communication is its major drawback. The Iridium system being developed by Motorola, for example, is expected to charge US$3 per minute, and the cost of the ground transceiver will be about US$S,OOO. This system has a target service date of 1998. If total coverage is an operational requirement, then satellite communication will be the only way to go.

TRAFFIC

MANAGEMENT

AND

CONTROL

Introduction The early work which provided the foundation for the Intelligent Transportation Systems is more than thirty years old. This work ranged from automated highways in 1960 [132], to electronic route guidance systems in 1968-1970 [133,134], to the Comprehensive Automobile Traffic

Enabling

Technologies

53

Control System (CACS) in 1979 [135], as well as the Autofarer Leitung und Information system (ALI) [136] and PROMETHEUS (1371. The evolution of intelligent transportation programs in the U.S., Japan, and Europe is discussed in more detail in [138]. Within the ITS framework, the public infrastructure portion has been commonly called the Advanced Traffic Management System (ATMS). It is the foundation for ITS, and will integrate with and support all other ITS technical aspects. ATMS technology impacts electronic toll collection, traffic surveillance, and adaptive traffic-signal control [139-1431. The functions encompassed by ATMS generally include: _ collection of data from vehicles to generate system-wide traffic information, - application of knowledge about the traffic flow, and the road infrastructure and its conditions, to optimize traffic operations, and - communications between the road infrastructure and the vehicles and travelers. The coordination of ATMS efforts in the U.S. have been driven by three major factors: (1) congressional funding for ITS activities, along with some specific congressional in.structlOIlS,

(2) ITS America, its working groups on ATMS, and its strategic plan, and (3) the 1J.S. DOT guidance, including management of funds for research, testing, and deployment of ATMS-related technologies. An excellent discussion of this topic is given by Ofstead [139]. In this section, we provide an overview of ATMS operational field trials, methodologies for modelling and designing traffic flow, and traffic data management. Operational

Field Trials

The goal of an ATMS is to improve the entire transportation system via adaptive timing of traffic lights, automated toll collection, and electronic displays and warning signs. The benefits include reduced traffic congestion and pollution, as well as improved traveler comfort, energy savings, and safety. It is estimated that ITS benefits per year will be 3,300 lives saved, 400,000 injuries avoided, 20 percent less congestion and 15 percent reduction of empty trucks [140]. The safety issues, in the context of the Demand for Road use, Accidents and their Gravity (DRAG) program in Canada, are discussed by Gaudry [141,142]. The author provides a primarily qualitative summary of a selected number of results obtained for the Province of Quebec: with the DRAG model. It compares different factors, e.g., pregnancy, drug and alcohol use, age, sex, in determining the likelihood of the occurrence of an accident. Gaudry notes in particular how the very structure of the model makes it possible to decompose the impact of each explanatory factor on exposure risk, as well as on accident frequency and severity, thus making it possible to detect the presence of substitution among the different dimensions of driving risk. The selection, made on the basis of potential interest for the establishment of responsibility for road accidents, may also have relevance for general accident analysis and prevention. An effective operational field test program known as FastTrac was recently conducted in a suburb of t,he Detroit, MI metropolitan area. TV cameras were placed in the 28 most congested intersections of the City of Troy. An image processing system called AutoScope. developed at the University of Minnesota, collects data on traffic flow rates and length of queues. The data are then communicated to a traffic control computer which runs the Sydney Coordinated Adaptive Traffic System (SCATS). SCATS adjusts the timing of the traffic signals to maximize flow rates and minimize queue lengths [144,145]. “The more sophisticated systems require no pavement adjustments and are monitored only by exception, everything is above ground, easy to get ‘to and easy to fix” according to Dick Beaubien, Transportation Director for Hubbell, Roth & C!ark, a Michigan engineering consulting firm that has studied traffic tie-ups in Oakland County, MI. The Fast-Trac system, introduced in mid-1992, has been reported to reduce peak-time commuting by 15-20 percent along the busiest artery, Rochester Road, which has 50,000 or more commuters.

A. GARCIA-ORTIZ et al.

54

The route guidance

system

Ah-Scout,

developed

by Siemens,

is being introduced

into the pro-

gram in an effort to merge the individual driver’s routing preferences into the overall traffic management system. Ali-Scout uses infrared receivers and transmitters to communicate routing and navigation included

information

700 vehicles,

about

to the vehicle. 1448 kilometers

The Berlin of Berlin

project

where Ali-Scout

streets

was first tested

with 2,000 infrared

beacons,

and

250 intersections. Collier and Weiland point out that “unlike the autonomous route guidance provided by TravTek and Advance, Ali-Scout accepts routing requests from vehicles via roadside infrared beacons, calculates routes centrally, and beams them back. Although trolled systems require less sophisticated gear inside cars, the cost of the required equipment can be significant” [143]. Other

examples

- ADVANCE

of operational (Advanced

field tests in the U.S. include Driver

and Vehicle

Advisory

the following Navigation

cars are to be used as traffic probes in a 647 square-kilometer

centrally coninfrastructure

[140]:

Concept),

Chicago

suburb

where

5000

via a 2-way

mobile radio link. The GPS, dead-reckoning and map matching are used to determine vehicle location; the information is communicated to the driver via a color display and voice [146]. Dead reckoning is a process of calculating a vehicle’s current location by keeping track of its motion away from a known starting point. Map matching is an artificial intelligence based process where the patterns of vehicle’s path are correlated with road patterns on a digital map. _ Advantage I-75, which covers the truck route from Canada and Michigan to Florida, along Interstate 75, and involves smart data collection and automatic vehicle identification. - Cellular Surveillance in Washington, DC, where cellular phones and their associated infrastructure serve as probes for traffic location, and speed and volume monitoring. - DIRECT (Driver Information Radio utilizing Experimental Communication Technology), where 30 test vehicles provide real-time traffic information I-94 between Detroit and the airport via cellular call-ins.

about

the traffic conditions

on

- Help/Crescent (Heavy-vehicle Electronic License Plate program), which facilitates commercial vehicle operations (CVO) along the Pacific coast, from Canada and Washington to Texas, along U.S. Interstate highways 5 and 10. In an effort to reduce regulatory and operational costs, 5000 trucks are equipped with computers and communication capabili(AVI). In ties which provide weigh-in-motion (WIM), and automatic vehicle identification addition, 40 sensors are placed at border crossings and strategic locations. - 1-80, covers from California to New York along U.S. interstate highway 80, and involves the development and deployment of a national truck network. - Pathfinder, is a collaborative effort between the FHwA, Caltrans, and General Motors, where 25 test vehicles equipped with Travel Pilot and an ETAK map are used in the Los Angeles area along the Santa Monica Freeway corridor. A two-way data communication link is used to send traffic information for vehicle navigation. - Smart Commuter, is a real-time car-pool and transit information system in Houston, Texas. - SMART Corridor, began as a three year Los Angeles demostration project in 1989. Traffic information and routes are communicated with cellular and teletext for five highway arterials spanning about fourteen miles. - Scandi (Surveillance, Control and Driver Information), begun in 1978 it involves traffic surveillance for about 52 kilometers of freeway in Detroit. This project uses 1,300 loop detectors, 12 video cameras, variable message signs, and ramp metering. - TravTek, a motorist information system and vehicle-based route guidance for a 3367 square kilometer area in Orlando, FL. One hundred test vehicles were deployed; the routes were communicated via synthetic voice and displayed on a map using arrows and text. The drivers are provided with alternative routes for their consideration. The in-vehicle equipment

Enabling

Technologies

map matching

capability,

includes

a map database,

program

was a collaborative

Association, Significant

FLDOT,

programs

effort

FHwA,

between

55

and route guidance

General

Motors,

algorithms.

the American

This

Automobile

and the cit,y of Orlando.

and field tests

in Europe

include:

DRIVE 11471, PROMETHEUS [137], Master, which has been in operation in the London area since fall of 1990. The Japanese efforts include AMTICS (Advanced Mobile Traffic Information and Communication System), and RACS (Road Automotive Communication the U.K.

Autoguide,

System)

version

of Ali-Scout,

and

%zfic

[135,140,143].

As evidenced

by the aforementioned

it is the most mature programs

area of ITS.

in California,

New York,

Colorado,

New Jersey,

Minnesota

efforts,

ATMS

In addition Connecticut,

Pennsylvania,

alone there are Guidestar,

is clearly

in the early deployment

to those mentioned, Delaware,

Virginia,

Travlink,

Missouri,

and Wisconsin.

Triology.

Ark,

phase;

are many more ATMS

Idaho, Maryland,

Washington

Genesis,

there

Minnesota,

For example,

in

and automated

ITMS,

CVO programs. Traffic

Modeling

and

From a modeling as the pred.iction tially

[148].

corresponds

The temporal

for trip planning

by DOT and vehicle

in the literature

travel during holidays, the ITS

nature traffic

from the graph:

are being exploited

research

Methodologies

point of view, the problem

of the behavior

to eastbound

can be discerned

addressed

Solution

of traffic

of dynamic

traffic flow assignment

of the flow is evident

in the data

a daily flow pattern

routing,

e.g., the ADVANCE

until now, is the seasonal

and the summer

scheduling, program.

or annual

and winter seasons.

at Electronics

& Space

Both

and by ITS A third

pattern

patterns of these

researchers

pattern,

which reflects

This last pattern Corp.

17 which

Two temporal

and a weekly flow pattern.

for road maintenance

and spa-

shown in Figure

along I-70 near the City of St. Louis.

personnel

being conducted

can be viewed

flow where the flow varies both temporally

not yet vacation

is incorporated

and Washington

Universlity

in in

St. Louis.

Day of the Month Hourof the Day

Figure 17. The periodicity of traffic flow evidences itself when vehicle counts obtalned through the use of road-side sensors are graphed as surface plots.

Temporal through

traffic

flow is due to work related

Friday between

and workplace when attempting

travel,

i.e., travel to and from work from Monday

the hours of 8 AM and 4 PM. Spatial

distributions

in a given area.

to model traffic flow, namely:

traffic flow is due to the population

As a result of this, some fundamental

issues arise

A. GARCIA-ORTIZet al.

56

-

Where to place traffic flow sensors. How to characterize the traffic flow. How to manage the traffic load. What on-line estimators to use for system identification

and parameter estimation,

e.g., a

stochastic formulation such as Kalman filtering, or an artificial neural network prediction. - How to perform vehicle routing and adjust traffic signals, e.g., use an optimal control formulation. - How to formulate traffic flow, e.g., as interconnected

pipes with adjustable

as discrete event dynamics; a system approach versus a mathematical combinatorial To be successful,

valves, or

programming

and

optimization. an ATMS will require a dynamic traffic model that can operate

in real

time, and reliably predict traffic congestion. Ben-Akiva has developed a dynamic traffic model that consists of an origin-destination module, a surveillance module, a congestion prediction module, and a control strategy generation module [149]. The core of the model is a flexible traffic simulation model. The distributed implementation of the model is also briefly discussed in his paper. Similar topics and issues for the ADVANCE project in Chicago are being addressed by Boyce et al. [150]. In general, the solution methodologies can be grouped into five categories: (1) (2) (3) (4) (5)

available for the dynamic traffic assignment

problem

computer simulation, mathematical programming, optimal control, artificial intelligence, and intelligent control.

Steven Underwood, from the University of Michigan, provides an overview of the first three methodologies in his 1993 paper [148]. In what follows, we review all five methodologies. Computer

simulation

This methodology is based on the use of dynamic traffic simulation (DTS) software. The desirable features of a DTS package include: representation of a street or highway network; description of lanes, turning movements, etc.; definition of dynamic link flows and demands; definition of link dynamics, including queuing, platooning, etc.; assorted data outputs, e.g., travel times, delays, emissions, fuel usage, etc.; definition of adaptive or fixed-time signal timing control; and description of vehicle routing by class [147]. Several packages were evaluated at an FHwA-sponsored workshop on DTS; they are: - INTEGRATION, and its derivative SAVaNT at the University of Michigan, is a discreteevent, deterministic simulation written in FORTRAN and C++. It is capable of handling a network of 10,000 nodes, 20,000 links, and 1 million vehicles, thus providing a mesoscopic level of detail. It incorporates an impedance function and traffic signal control. The link dynamics include queueing at signals, platoon dispersion, and shock waves. The package is useful in route-guidance evaluation applications, and is being used for the evaluation of the U.S. ITS Architecture Development program concepts [119,120]. The software executes on a RISC 6000 workstation. - THOREAU, formerly VTAC, was developed by The MITRE Corporation. It is discreteevent, stochastic simulation based on the MODSIM software package, and capable of handling a network of 100 nodes, 500 links, and 10,000 vehicles, thus providing a microscopic level of detail. The model incorporates lanes and car-following controls, Floyd’s algorithm for optimization, and link dynamics, including lane changing, weaving, merging, acceleration and deceleration. It is useful for evaluating traffic control systems. The

Enabling

software

runs on Sun Microsystems

to evaluate

the U.S. ITS

‘Technologies

57

Spare station.

architectural

Like INTEGRATION,

it is being usec

concepts.

_

TRAFLO developed by the FHwA. DYNASMART developed by the University of Texas at Austin. ~ Dynamic !l+afic Model developed by the Massachussets Institute of Technology. METANET developed by the Technological University of Munich. SATURN and CONTRAM developed in the U.K.

-

Ongoing

research

in this area includes

network simulation particle

methodologies

traffic simulation

(MICRO)

model.

model (MPSM),

These

the capability

the work of Chang

for application

methodologies

of simulating

(2) modified

incorporate

both

et al. who have identified

to ITS [151]. The methodologies MPSM

(M-MPSM);

the following

freeways

and surface

three :Lraffic

are: (1) macro-

and (3) microscopic

features:

street

networks

as an integrated

network, ~ a path processing

capability

with and without ~ the capability the software The Georgia Ingle

of simulating execution

Institute

mentation

decisions

traffic

associated

route choice behavior

system

(ATIS),

and

operations.

is also working

of the architecture, model,

on an ATMS

capabilities,

with the development

of the traffic

driver’s

information

several subnet8works at differing levels of detail so as to increase

of Technology

simulator.

technical

of their simulator

the database,

In their

challenges, [152].

paper,

and imple-

They

also give a

the displays,

and the associated

simulated

highways

has been demonstrated

video coverage.

The use ,of simulations maswamy

et al. [153].

multilane

highway.

for the design of automated Of particular

Their

assignment highway

strategies

systems

Vaughn collected ulation

interrelationships

sequential

collected

accuracy.

assumed

An analysis

influence

route choice behavior

It had been

information

among the different

significantly that

update

The resulting

model of route choice behavior

research

Mathematical

that

to properly

and dynamic.

and dynamic

The vehicle routing

was specified

had a prediction experience

identify

at

on the dat,a to inves-

Their at,tempt to model resulted

in mixed results.

based

and incorporated

on experience, into the model.

rate that was 79 percent

has little effect on current

these important

sim-

behavior

to develop an understanding

of the system

Applications routing,

to traffic to dynamic

and optimal

problem

management

This topic was discussed

(VRP)

may at first, not seem to be directly in platoons,

function

previous

of this methodology

both static

section of this document. stochastic

updating

route choice

and learning.

knowledge

in which. they

using a PC-based

was performed

in an attempt

driving.

learning

accurate,

route choice.

relationships.

programming

An application problems,

is required

pretrip

of lane

of future urban

an experiment

of an ATIS,

on drivers’

of variance

their

an information

Further

conducted

variables

by a vehicle

of automated

using a binary logic model formulation

drivers

also indicated

and development

introduction

route choice behavior

and therefo’re but the results

of the path taken

role in the planning

of California-Davis,

by Ra-

for an automated

believe that the development

driver route choices under the influence

three, levels of information of what factors

The authors

lanes, and in the gradual

et al., at the University data about

the management

corridor.

will play an important

with multiple

is the work in lane assignment

represents

multilane

[15/L]. The experiment

tigate

interest

approach

after it enters an automated

166]

the individual

traveler

speed for real-time

et al. provide a summary

brief description

for representing

access to an advanced

as part of traffic routing,

1s in the solution in the general

vehicle routing

adaptive

policies

to ATMS;

then it is possible

path

include several developments

for dynamic

as defined by the operations

applicable

of shortest

sense in a previous

however,

vehicle

research

routing

(OR)

community

if one was to cluster

for the problem

of routing

in

[155-

vehicles

individual

cars

58

A. GARCIA-ORTIZet al.

from the cluster to be viewed as a VRP. This approach, however, involves too much computation time to be feasible in a real-time environment. Relevant work in mathematical programming includes the efforts of Davis and Nihan, from the University of Minnesota,

who have posed a general stochastic

assignment model that includes

as special cases most models which have appeared in the literature.

They have verified that

the probability distributions of an equivalent Markovian model converge to a stationary distribution [157]. Their work verifies that as the number of individual travelers becomes large, the general model can be approximated by the sum of a nonlinear deterministic process and a timevarying, linear Gaussian process. The stationary distribution of this approximation is readily characterized, and the approximation also provides a means for employing linear system methods to estimate model parameters from a set of observed traffic counts. For the case where the route choice probabilities are given by the multinomial logic function, computationally feasible procedures for implementing the approximate model are available. In the area of linear programming (LP), ex p erience in solving a 1 million variable LP problem has been described by Bixby et al. [159]. The problem is the LP relaxation of a set partitioning problem arising from an airline crew scheduling application. The scheme described by Bixby requires successive solutions of small subproblems, and yields a procedure which has little growth in solution time in terms of the number of variables. Their paper relates their experience using the simplex method as implemented in CPLEX, an interior point method as implemented in OBl, and a hybrid interior point/simplex approach. The resulting procedure illustrates the power of an interior point/simplex combination for solving very large-scale LP problems. Their article, and its methods, should be kept in mind when dealing with a shortest path problem. The methods mentioned were implemented on a CRAY. Along the same lines, Bixby has provided an elementary description of the bounded-variable simplex method, and a rather detailed discussion of some of the numerical characteristics of the netlib test problems [160]. These problems form the basis for the computational tests. The paper provides a description of the method used by CPLEX for constructing an initial basis. Bertsimas and van Ryzin have proposed and analyzed a generic mathematical model for dynamic, stochastic, VRPs, called the dynamic TRP (DTRP) model [161]. The model is motivated by applications in which the objective is to minimize the wait for service in a stochastic and dynamically changing environment. This is a departure from the classical VRP where one seeks to minimize total travel time in a static, deterministic environment. Potential areas of application of this work include repair, inventory, emergency service, and scheduling problems. Their paper defines the DTRP, and proposes and analyzes several policies for solving the DTRP. A provably optimal policy in light traffic, and several policies with system times within a constant factor of the optimal policy in heavy traffic are presented. The authors also show that the waiting time grows much faster than in traditional queues as the traffic intensity increases, yet the stability condition does not depend on the system geometry. The objective function used formulates the minimization of the total travel time instead of the maximization traffic flow, and it also routes vehicles from a central depot. These authors have also extended their analysis of the DTRP in several other directions [162]. They have analyzed the problem of m identical vehicles with unlimited capacity, and show that in heavy traffic the system time is reduced by a factor of mv2 over the single-server case. One of the policies developed improves, by a factor of two, on the best known system time for the single-server case. They have also considered the case in which each vehicle can serve at most q customers before returning to a depot. In contrast to the uncapacitated case, this case shows that the stability condition depends strongly on the geometry of the region. Several policies which have system times within a constant factor of the optimum in heavy traffic have been proposed. Their paper also discusses extending the results to mixed travel cost and system time objectives. Another set of relevant models are system optimal dynamic assignment models. These models address the situation where time-dependent origin-destination goals are assigned from multiple

Enabling Technologies origins

to multiple

59

destinations through the links of a network so as to minimize the total system

cost. Lasdon and Luo, at the University of Texas-Austin, have formulated and solved a class of discrete time, nonlinear models [163]. A GAMS implementation is described in their paper, along with solution features of both continuous nonlinear and piecewise linear versions of the model. While the problems they have solved are relatively small, they have led to large optimiz,ation problems with considerable solution times requirements. Birge and Ho, at the University of Michigan-Ann Arbor, have worked out a method for finding optimal flows in a dynamic network with random inputs into the system, and congestion limits on flow [164]. This model has been used in deterministic settings to represent dynamic traffic assignment, and job shop routing. Their paper builds on the deterministic results to show that a globally optimal solution for the stochastic problem may be obtained by a sequence of linear optimizations.

A decomposition algorithm that efficiently solves problems with large-scale deter-

ministic equivalents of up to 66,000 variables is presented. The advantages of this methodology are its capability to work on a system optimal problem, and the results include an algorithm that runs in a reasonable amount of time. The approach assumes, however, a single destination, and the hardware used to implement it is not discussed. An interesting approach is that followed by Carey and Srinivasan which attempts to force traffic flow in a desired manner by imposing tolls [165]. For each arc and path in a congiested network wit,h time-varying flows, they derive system marginal costs, user perceived costs, and user ext,ernality costs. They also obtain a set of optimal congestion tolls and flow controls which may be used to shift the user’s selected flows toward a socially preferred pattern. An important way in which these results differ from the usual static analysis is that the social cost externality depends not only on the level of congestion, but also on the rate of increase or decrease of congestion. As a result, the congestion tolls or tariffs which are usually proposed or advocated based on static analysis seem to be inappropriate. Results illustrated in the paper apply to a small road network example. Optimal

control

In the past, the macroscopic modeling of dynamic traffic patterns and the control of multidestination traffic networks have been considered to be very challenging problems [167-1711. One case in point is that of Papageorgio et al. who have dealt with an optimal control formulation for multidestination traffic networks [167,168]. Dynamic modeling, assignment, and route guidance results are presented in their paper, and applied to traffic management in a boulevard in Paris, France. Two continuous time formulations of the dynamic traffic assignment problem are described by Friesz er al. [166]. One corresponds to a system optimization, and the other to a version of user optimization on a single mode network using optimal control theory. Pontryagin’s necessary conditions are analyzed and given economic interpretations that correspond to intuitive notions regarding the dynamic system being optimized and the dynamic user optimized traffic flow patterns. Notably, they offer the first dynamic generalization of Beckmann’s equivalent optimization problem for static user optimized traffic assignment in the form of an optimal control problem. Their analysis further establishes that the constraint qualification and convexity requirements for the Hamiltonian, which together ensure that the necessary conditions are also sufficient, are sat,isfied under commonly encountered regularity conditions. Artificial

intelligence

A general review of appliations of artificial intelligence (AI) methodologies to ITS is presented by Rock et al. [172]. The authors provide a brief overview of intelligent information control through expert systems, neural nets, fuzzy logic, genetic optimization, and data fusion. They point out that the magnitude and scope of ITS problems pushes the AI state-of-the-art. The

A. GARCIA-ORTIZet al.

60

principal challenge, at the data fusion stage, is the need to handle tremendous data noise, as well as signal noise interference, and incomplete and missing information. Heuristic solutions to search and path-finding problems are discussed by Stewart [37] and Korf [27]. Additional relevant discussions are provided by Dechter [45], Kanal [26], Schildt 1341,and Tanimoto [38]. The A* algorithm is perhaps the most widely implemented form of heuristic search. Its characteristic feature is the evaluation function, f(i), which is the sum of two components: (1) the estimated minimum cost, g(i), of a path from the initial state to the current node i on the Open list, and (2) the estimated cost to go from i to the destination node. The first component can be calculated if the search space is a tree, or it can be approximated if the search space is a graph. The second component must be defined in a way that it takes the domain features into consideration, and the search proved to be admissible.

The original

work on A* was published in 1968 by Hart, Nilsson and Raphael [47] with a correction published in 1972. More recent results, extensions, and implementations are discussed by other authors, e.g., Korf [33], Stewart et al. [35], Chang et al. [151], and Nelson et al. A fairly in-depth discussion of the application of AI to path planning is given in an earlier section of this document. Chen et al. have developed an approach for applying Genetic Algorithms (GA) to scheduling problems [173]. It is based on the generation of a GA-based heuristic for continuous flow, shop problems with total flow time as the criterion. The effects of several crucial factors of GA on the performance of the heuristics for the problem are explored in detail in their paper. The computational experience of the heuristic provides several observations of the application of GA, and strongly supports that the applications of GA are problem specific. It also shows that GA can be a good technique for scheduling problems. A neural network (NN) approach to the coordinated signalization of surface street lights for adaptively managing special event traffic flows in the Atlanta area is presented by Gilmore et al. [174]. The Traffic Event Response and Management for Intelligent Navigation Utilizing Signals (TERMINUS) system represents each street intersection as a neuron with the streets themselves comprising the neural interconnections. Using an energy function that incorporates traffic flow rates, street segment capacities, and weighted street priorities, TERMINUS produces optimal traffic signal settings based upon the traffic simulation inputs. Their paper describes the three components of TERMINUS, and reinforces each with results achieved through extensive simulation and system adaptation. A major task in road accident remedial work is the selection of sites for treatment, and the identification of appropriate treatments for them. This particular application has been adressed by Heydecker and Wu [175]. Their approach involves both skilled judgment, and the processing of large quantities of accident data. A knowledge-based system has been developed to automate the routing parts of this task. The main feature of the system is the integration, within a knowledge-based system, of a database system, empirical Bayesian statistical techniques, and Bayesian inference with uncertain inference. This is in accordance with the character of road accident remedial work in which evidence is uncertain due to its derivation from accident data, which are both sparse and subject to stochastic variation. The automation of the routine parts of this work using the present system should speed them up significantly and make them more comprehensive. The major payoff of this approach is that it will enable road safety engineers to concentrate on applying their judgment and skills to more technically challenging problems. Because relevant corporate expertise and experience is incorporated in the system, it allows the skills of the most effective practitioners to be shared more widely than is possible at the moment. Intelligent

control

Dynamic traffic prediction and control provides a promising area for research, development and the application of intelligent controllers, i.e., hierarchical controllers which utilize AI, OR and

Enabling

optimal

control

prediction,

theory.

system

Leonard

approach

system,

receiving

operator-inputs)

Evaluation

of ATMS

and, ultima.tely,

strategies

“functions

technology

of dynamic

as the central

nervous

from a variety

(image

processing,

of large systems

strategies.

were developed

for ‘off-line’

analysis

on the types of signal control systems which can be simulated” information

processing

based model of traffic flow, for the evaluation

algorithm,

of systems

(on-street

probes)

sources.

controllers

In general,

existing

and typically

impose

[176]. Their focus is the

based on a macroscopic,

of coordinated

pla.toon-

and uncoordinated

traffic

controllers.

A brief overview

of a real-time,

been used for autonomous of the hierarchical following

vehicles

arrangement

characteristic

control

system

is presented

(RCS)

reference

by Albus

of seven heterogeneous

model architecture

A layer consists world modeling,

of control.

of one or more computational and task decomposition

database

as applied to vehicle

and message

functions.

are: Highwav Control Architecture Highway system control center

planning

Region control

5

Road segment planning

Segment

4

Vehicle path planning

Road section

3

Steering

and attention

2

Steering

dynamics

1

Actuator

control

receivers.

Along very similar for the ITS control

coordination

for example,

traveler

advisory

magnetic

This RCS reference lines of reasoning,

level, i.e., the vehicle, Laver

measured

video and infrared

Varaiya

robotics

has defined

system

cameras, system

testbed

radar

and

velocity,

and

architecture

vehicle.

a four layer, hierarchical

structure

[178]. In this case the layers, and their key features.,

to the top level, i.e., the road network,

are:

and communications

by the sensors are: position,

ground vehicle,

from

are:

Feature

Regulation

Feedback

Planning

Coordination

Link

Control of aggregate

Network

Route assignment

of each of these

for the highway

model has been used as a control

Corps unmanned, architecture

the actuators

communication

signs, radar transmitters

loop detectors,

The state variables

system

Lane control High bandwidth

architecture, are:

control

Signal control

servos

indicators,

the

system

The seven levels in the hierarchy,

Trip segment planning

for the U.S. Army/Marine

Details

passing

within

layers by a communications

6

class for each vehicle.

the bottom

wit,hin and between

Vehicle Control Architecture

The sensors

communications

has the

sensory processing,

are interconnected

Trip destination

lane-use

transmitters.

services

The modules

7

In the highway traffic lights,

consists

layer

loops,

nodes, and each node contains

modules.

and highway control,

M

Each

modules:

and the nodes are interconnected

that provides

which has

et al. [177]. The architecture layers

_ timing and bandwidth of sensory processing and servo control _ range and resolution of world model maps, and _ spatial-temporal range and resolution of goals and plans.

nodes,

of the overall

of traffic actuated

optimization

restrictions

actuated

system

‘real-time’

and optimization of a “real-time”

recognition,

and regulation.

of standard

in-vehicle

simulation development

tools

of pattern

and adaptation

inputs

involves simulation

development

combination

and optimization,

and combining

and advanced

61

calls for a judicious

goal selection

et al. point out that an ATMS

transportation loops,

The

identification,

Technologies

layers,

presented

in his paper.

The

discussed.

They include road-side

laws for braking, of movement

steering

monitors

needs

vehicles

traffic flow

and admission

as well as experimental

hardware

and throttle

of neighboring control

results

for the above

for measuring

from the PATH

control

structure

traffic conditions,

project

are

are also briefly

sensors for measuring

A. GARCIA-ORTIZ et al.

62

relative distance and speed, and inter-vehicle and a delay of about 20 milliseconds. A different Control

hierarchichal

at Washington

approach University

communication

with a range of about

is being used by the Center

for Optimization

in St. Louis, which has been conducting

60 meters,

and Semantic

research

and devel-

opment jointly with Electronics & Space Corp. in the areas of vehicle routing and maneuvering, road network analysis, and traffic management [179-1821. The objective of the work is to develop layers of prediction,

planning,

sensing and management. utilizing semantic control Semantic knowledge

control,

the plant

modules

for the problem

of traffic flow

Researchers solve large-scale, complex, and time-dependent theory and various optimization techniques.

control theory deals with about

and optimization

large scale control

to be controlled

and about

problems

the methods

where

formalization

of control

geometry,

control theory

and game theory, together

of the

are particularly

difficult [183-1881. In general, the approach to the solution of such problems involves a judicious combination of classical mathematical methodologies such as mathematical ming, computational

problems

the use of program-

with AI paradigms

such

as fuzzy system theory, artificial neural networks, knowledge-based systems and logic programming [189]. A semantic controller consists of three modules for identification, goal selection, and adaptation of the system. The functional diagram of a semantic controller for ATMS applications is depicted

in Figure

18. DISTURBANCE

+

OUTPUT

Figure 18. A semantic control system consists of a System Identifier, a Goal Selector, a System Adapter, and one or more control systems/laws.

In a traffic management application, the Identifier levels, travel times, and incident detection/localization.

measures or predicts the road congestion The Goal Selector handles traffic routing

for maximum use of available network capacity. The Adapter provides driver support for shortest path routing and steering control. At present, a model of the interstate highway system for the St. Louis metropolitan area is being developed based on historical traffic data provided by the Missouri Highway and Transportation Department and traffic sensors deployed by Electronics & Space Corp. Figure 19 shows one of the data representation schemes being used in the development of a semantic ATMS, namely, isoflow contours. The ATMS incorporates a variety of computational technologies. The Identifier has a stochastic forecasting/prediction module, rules for incident detection, and an artificial neural network based estimator. The Goal Selector consists of shortest-path and optimal control algorithms. The control laws in the Adapter are being developed for two situations: (1) vehicle control within a platoon, (2) neurocontrol of a single vehicle.

and

Traffic Data Management It bodes well to model traffic flow, and to apply any one of the various methodologies described above, but the vast amounts of data that will be collected under the guise of ITS must also be

Enabling

63

Technologies

H 6cOo-7LWo

n

5000-6000

n

40%Woo 3ooo-4oCKl 2000-3000

0

looo-2ooo

n

o-loGil

30

1

Day of the Month Figure 19. Increased of isoflow curves.

visibility

into traffic flow patterns

is obtained

through

the use

properly managed if we are to reap any benefits from the overall enterprise. A brief overview of the technological advances in digital map databases, and the data processing capacity of microcomputers, was discussed earlier in this document. In what follows, we further discuss some releva,nt work concerning data management. Sweeney and Zavoli are contributing to a dramatic revolution in the ability to provide geographically-based. solutions to transportation problems [190]. They point out that “digital road-map database structures are now available that reduce the data storage requirements by an order of magnitude, and data access times by two orders of magnitude compared to previous approaches. Furthermore, digital hardware technology now provides remarkable computational and storage capacities in small packages at reasonable prices in notebook and in palmtop computers; combined with hard disks, CD ROM, and RAM cards. Even more dramatic advances are expected in the near future. These capabilities, combined with navigation systems (such as GPS, map matching, and radio location), permit users to obtain and utilize precise, geographically-based information., regardless of their location. Such approaches are useful in a wide variety of applications includ.ing GIS data collection and utilization, congestion management, transit planning and scheduling, traffic data collection systems, traveler information systems, fleet management, and many more. Present and future trends in geographically-based information and its application to transportation systems are discussed, and how these trends are becoming evident from work in IVHS, navigation, and GIS programs.” Ortuzar et al. are addressing the problem of determining appropriate levels of data aggregation for two key travel demand forecasting models: (1) those that predict destination choice or trip distribution, (2) those that predict transportation mode choice [191,192].

and

These choice processes may be treated either separately or, as is now becoming more common, in a joint modeling procedure. They were chosen for study because of their intrinsic importance, and because of the availability of results from a number of other studies that serve to illustrate key points. Their paper gives indications for appropriate aggregation levels to be used in empirical studies. The authors explore methods for updating and transferring models from one context to another, ensuring that they are adequate for the new conditions. A necessary step in the development of any type of system is the assessment of data and information requirements. For ITS applications, this information is needed to evaluate the suit-

A. GARCIA-ORTIZet al.

64

ability of available technologies, ITS elements, and architectures for the design of an operational system. Neenan and Huang have addressed the information requirements for the development of an integrated

public transit/traffic

management

and traveler information

system [193]. The

integration effort includes the automated transfer of operations and management information between agencies, and to the general public, so as to realize improvements in the efficiency of the transportation network. In their paper, the availability of the required data is assessed, and existing and potential data sources are identified. At a more specific level, Choy et al. discuss the information ments of an ATIS [194]. In this case, efficient information environment,

and at different geographical

retrieval and updating require-

retrieval and updating in a dynamic

scales, is required.

The authors point out in their

paper that some problems in ATIS can be solved based on the functionalities systems.

provided by GIS

However, extra requirements such as real-time response are not readily met by existing

GIS systems.

The authors have investigated

the use of GIS-based

systems for applications

in

ATIS, and they have proposed a new system architecture based on existing GIS technology and distributed computing technology. Issues on data modeling, data representation, storage and retrieval, data aggregation, and parallel processing of online queries in the proposed GIS-based systems are also discussed in their paper. Laraqui et al. [195], from Telia Research in Sweden, have proposed a protocol architecture for the European Integrated Road Transport and Traffic Environment (IRTE)/Intelligent Vehicle Highway System (IVHS). I n order to maximize integration at the application and subnetwork levels, the authors use the IS0 8473 Connectionless Network Protocol recommendation. They claim that their approach strikes a balance between conventional protocol composition and IRTE industrial concerns. By implementing the favored protocol architecture in a testbed, they have shown the viability of a such an approach. On a different note, a basic assumption of ITS is that the use of route guidance and traveler information systems will lead to a reduction in traffic congestion levels. Arnott et al. question that this is indeed the case; in fact, they argue that the information provided can cause drivers to change their departure times in such a way as to exacerbate the traffic congestion problem [196]. They point out the need to consider the general equilibrium effects of information. A simple model has been developed for the morning rush hour, in which commuters choose a departure time and one of two routes to work, the capacities of which are stochastic. The results show that while the expected travel costs are reduced by perfectly informing all drivers about route capacities, this is not necessarily the case when imperfect information is provided. Human response to the information provided about the road network is therefore an important consideration in the development of ITS. Kelly and Ford point out that in developing the future ITS traffic management systems, the most challenging traffic data interpretations and responses will remain the responsibility of human operators [197]. Their studies have developed a usercentered plan for implementing ITS technology, and explore examples of human factors pitfalls awaiting the designer who does so.

VEHICLE

CONTROL

Introduction Advanced Vehicle Control Systems (AVCS) involve the use of a variety of sensors, computers, and electromechanical actuators to control the engine/transmission, brakes, and steering system of a vehicle. The concept of an automated vehicle was first displayed at the General Motors’ Futurama exhibit during the 1939 World’s Fair in New York where it attracted considerable interest. Vehicle control in the U.S., Japan, and England spans a period of over forty years beginning in the 1950s with experimental vehicles, and continuing into the 1960s and 1970s [132,198-2011. However, in the early 1970s considerably more emphasis was placed on personal, rapid transit [198]. Several comprehensive collections of papers on ITS and AVCS appeared in the 1970,

Enabling Technologies

65

1979, and 1991 special issues of the IEEE Transactions on Vehicular Technology [201-2041; additional publications appeared in various proceedings of the IEEE sponsored Conference on Decision and Control, and the American Control Conference.

From an overall ITS perspective,

AVCS has

received less attention than the other two near-term areas of ATMS and ATIS. AVCS holds an immense potential

for providing many social benefits in terms of improved

safety and reduced environmental pollution, as well as improved road network throughput. specific topics covered by AVCS are indicated in Table 14.

The

Table 14. Topics covered under AVCS. Intelligent cruise control Lane/edge

sensing

Lateral control, longitudinal,

and intersection

Near obstacle detection system Blind spot detection Speed and headway maintenance Automatic

braking

Collision warning Improved ride comfort Vision enhancement Automated

highway systems

Platooning Precrash restraint deployment Safety readiness

The AVCS Committee of ITS America has identified several vehicle control research and development activities and issues which require attention; they are presented here [198]: l

The activities

needed for enabling technologies include

~ definition of performance requirements, _ identification and evaluation of promising existing technologies, ~ identification

of “gaps” in current technologies,

~ basic research and development on needed technologies, and l

~ adaptation of existing technologies to AVCS needs. The target products need ~ definition of performance requirements,

l

~ selection of enabling technologies to use, _ product design, development, testing and marketing. The AVCS will need ~ definition of performance requirements, ~ concept design and analysis, _ selection of target products to incorporate,

l

~ research and design of system architecture, _ coordination of public and private sectors. Constraints on AVCS include

and

- cost, _ reliability, fault tolerance, and safety implications, _ environmental hardening in order to cope with inhospitable conditions; e.g., wide ranges of temperatures, vibration, acoustic and electromechanical noise, a.dverse weather conditions, dust, fog, etc.

66 l

A. GARCIA-ORTIZ et al.

AVCS enabling technologies include (a) Sensors: - Ranging devices to detect spacing and velocity differences in all relevant directions around a vehicle; with a required range of between 1 to 100 meters, an accuracy of 1 percent, and a sampling rate of at least 20 Hz, and the all weather capability. - Obstacle detection devices for avoidance of accidents. _ Lane sensing to detect the lateral position of a vehicle relative to the center of the lane; with an accuracy of 1 centimeter for small deviation and perhaps 10 centimeters for large deviations. _ Vision enhancement to improve driver’s visibility and providing improved early obstacle detection. - Real-time road friction sensing to measure the coefficient of friction between the tires and the road in order to improve vehicle handling in response to road conditions (snow, ice, sand, oil, water). _ Absolute location sensing for routing as well as regulating maneuvers. - Absolute velocity vector, insensitive to tire slip and loss of traction; this system would aid in distinguishing the longitudinal and lateral components of motion. _ Accelerometers to accurately measure (perhaps within 1 percent) vehicle longitudinal and lateral accelerations, compensated for road geometry effects. _ Angular rotation rate to measure yaw rate in particular (a very useful measurement for vehicle lateral control), _ Linear displacement measurement of suspension deflections and steering system motions to verify that the vehicle tracks the commanded trajectory. _ Driver performance to identify alertness of drivers and their ability to control the vehicle safely. (b) Communications devices: _ Vehicle-vehicle communications link for coordinated control and maneuvering as well as to warn of immediate dangers and obstacles. - Vehicle-roadway communications. (c) Computational devices: - Time and space efficient processing of sensor data, computation of control actions (commands to actuators or drivers display). Here also the primary issues remain high reliability, low cost and robust operation. (d) Electromechanical actuators: _ Actuation of engine (throttle and fuel injection), brakes, and steering system. _ Current technologies include antilock brakes, traction control and 4-wheel steering. (e) Software and systems technologies: _ Reliable, fault-tolerant hardware/software system design within tight cost constraints. - Fault detection and accommodation; self-diagnosis capability combined with fall-back modes of operation to accommodate faults. - Data fusion software to combine the outputs of various sensors with their different accuracies, error characteristics and failure modes. _ Threat analysis and prediction; e.g., assessment of other vehicles anticipated motion, moving obstacle avoidance, etc. _ Robust nonlinear and adaptive control design. - Human user interface designs. - Automatics trip routing and scheduling. _ System integration architecture.

Enabling

In addition to model, (1)

to the above activities

a.nalyze, predict

accident

Technologies

67

and issues, a considerable

and optimize

amount

the vehicle performance.

of data will be required

These

data include:

reports,

(2) vehicle dynamic response and characteristics, (3) driver response characteristics, (4)

road geometry

(5) statistically (6) traffic

and surface

conditions,

valid data for component

reliabilities,

flows and demands.

The data will be needed to develop alternative teraction.

AVCS models

and designs at various levels of in-

Several of these issues have already been addressed

In what follows, we provide a brief overview of models Vehicle

in earlier sections

A variety Berkeley

of vehicle models

have been reported

are those written

(see [207-2111

and hence,

and references

and Robust

An important desired

commanded

tracking

controller

ating

conditions,

robust

nonlinear

autonomous

for AVCS.

path that

and external control

in numerical

Another

design approach

rc’ar steering

case studies

is based on m-synthesis

theory.

the effect

construction

of the control

(1) Linearization ratio,

Experimental

results

Platoon

paper,

and the performance

The

time varying,

and Guldner

oper-

present

a

of the proposed

algorithm

robust

control

and traction?

i.e., active

of steering

front and rear wheels, on a four-wheel Ono et al. have developed

force on cornering

the steering

for the robust

show improved

t,ests, exhibit

to follow a

on the road surface.

Ackermann

drive vehicle

a nonlinear

force for each tire

vehicle model via a nonlinear

and various turning

during slalom tests,

papers.

vehicle

[206]

The

is based on the following two steps:

namely

of m-synthesis

utilized

and possibly

transformation

control

control

split

strategy.

design to render

the AVCS

insensitive

motions.

performance

over an H-infinity

the front steer angle and yaw velocity

good stability

which trans-

angle of the rear wheel and the torque

into a pseudo input in order to design the integrated

Application

as robustness

of driving

of the nonlinear

to disturbances responses

controller

guideline

to uncertain,

For this purpose,

strategy

forms two real inputs,

to the original

for a sedan car.

for the integrated

model

expresses

is the dynamic

In their

with torque split control between

which

in UC-

extensive,

based on a sliding mode theory which can be applied t,o various

[212]. The design procedure

is illustrated

reader is referred

by a reference

with respect

disturbances.

strategy

we have

Design

in the AVCS

is delineated

papers that

The details of these models are rather

Controller

component

Several

[205], Ono [206], and the PATH project

here. The interested

has to be robust

vehicles

therein).

Nonlinear

system

in the literature.

by Ackermann

they are not included

Nonlinear

design.

Plots

of the time

during lane changes,

,as well

results.

Control

One of several ways that have been proposed to increase the traffic capacity organize

vehicles

together

as a group, with an intra-platoon

about

of this document.

and design methodologies

Models

found of interest

(2)

and

60 meters.

in tightly-spaced Typically

72 km/hr through maximum

control

A platoon spacing

consists

of l-2

an automated

highway

architecture

system

consisting

and an interplatoon

per platoon.

lane would yield a three-fold

of road netwoks is to

of one or more vehicles

meters,

there would be 15 to 20 vehicles

flow of more than 6,000 vehicles

For the fully automated hierarchical

platoons.

increase

A platoon

traveling spacing

of

traveling

at

in road capacity,

with a

per hour 1177.208%2111 f (AHS),

Varaiya

of regulation,

et al. have proposed

planning,

a four-layered

link, and network

layers [178].

A. GARCIA-ORTIZet al.

68

The regulation layer’s task is to execute a set of feedback laws for throttle, braking, and steering. The design of the control laws is carried out within this layer to meet five objectives: (1) track the velocity setpoint computed by the link layer, (2) accelerate to merge, (3) decelerate to split, (4) lane change, and (5) ensure follower spacing control. Other approaches include design of vehicle following controllers which maintain a desired spacing between surrounding vehicles.

Earlier work in this area involved a controller design for a

single-mass model where the dynamics of the propulsion system were ignored [213]. In 1979, using a simplified model of the engine, Shladover designed a linear vehicle-following controller. Asymptotic

platoon stability was guaranteed when the leading vehicle’s velocity and acceleration

is available to other cars in the platoon, and if the nonlinear drag forces are ignored. Using the same model with nonlinearities and feedback linearization techniques, Desoer and Sheikholeslam [214] proved the asymptotic stability of a platoon when feedforward information is available. In this special issue, two papers by Stotsky, Chien, et al. present work in two of these areas: (1) individual vehicle stability and asymptotic platoon stability, and (2) longitudinal vehicle control within a platoon. The results of experiments carried out on a platoon control system developed in the PATH project are presented by McMahon [215]. The integrated platoon control system includes a control system, communication system, data acquisition system, and various sensors including a radar system. The control algorithm was developed using a modified sliding control method which compensates for the inherent nonlinearities that exist in the automobile plant. The sliding mode controller consists of a feedback linearization transformation and subsequent computation of PID gains to achieve vehicle following. Earlier simulation results had shown that the hybrid throttle and brake controller would successfully control platoons of 2 and 4 cars. During field trials, using 2 cars, the automated vehicle satisfactorily tracked the manually driven, lead vehicle through two different kinds of maneuvers. Neural

Nets,

Fuzzy

Logic,

and Genetic

Algorithms

The main motivation for the application of FLT to AVCS is to capture the rules-of-thumb that driver’s ordinarily apply to control a vehicle. As Zadeh has pointed out, parallel parking a car is a very good example of how people can rather easily learn to park, whereas such a problem is impossible to solve by exact optimal control methods. Another appealing feature of FLCs is their robustness properties which overtime outperform nonlinear PID control. Bosacchi and Masaki have discussed the potential of fuzzy logic technology (FLT) for ITS [216]. In their discussion, they highlight some relevant roles of FLT and illustrate them with examples from recent works on the subject. They also point out relevant publications where FLT is applied to driver’s assisted vehicle control, ATIS, and ATMS. A neurocontroller which learns to backup a truck to a dock was developed in 1989 by Nguyen and Widrow. This type of problem is a nonlinear control problem with no previously known solution The methodology used is based on the backpropagation-through-time algorithm proposed by Paul Werbos in 1990. The truck’s motion is broken down into time steps; the training algorithm works backward, from the final docking position to the starting position. There are two phases at each time step: (1) training of an emulator which attempts to model the truck, and (2) controller adjustment while keeping the emulator temporarily fixed. While this was a very novel and powerful methodology, it suffered a great deal from sensitivity to the accuracy of the emulator, and the errors were cumulative. Furthermore, control of forward

EnablingTechnologies vehicle motion with more complicated

69

braking, lateral and longitudinal motion control would

make the above problem extremely challenging. The truck-backer-upper

problem was revisited in 1992 by Kosko who developed a fuzzy logic

controller (FLC) that is not sensitive to emulator accuracy [217,218]. Dickerson and Kosko have also described a fuzzy system for throttle control of cars in platoons [219]. The FLC is used for velocity control, gap control and car following. It is also pointed out that additive fuzzy systems can uniformly approximate any continuous or measurable function [220]. This approximation

property provides a “model-free”

estimation of a control function.

The authors

use ellipsoidal covariance learning to find fuzzy rules and sets from the vehicle input-output data. This methodology covers the state-space with ellipsoidal fuzzy sets. The function approximation improves as the number of ellipsoids increases, and the ellipsoidal learning tunes the fuzzy rules and sets for cars of different sizes or engine types. Incorporation of the brake and steering systems is posed as the next phase in the design. Theoretical and practical applications of artificial neural networks (ANN) have had an enormous revival in recent years. Their use for approximation and modeling of “stat,? systems has been extensively studied. From a theoretical point of view it has been proved that, even with one layer, feedforward ANNs with an appropriately chosen number of units can approximate any continuous function over a compact domain. These networks can approximate arbitrary nonlinear mappings, generalize, attenuate noise, cluster data, handle both quantitative and/or qualitative data, and are applicable to multivariable systems. For example, a 21-neuron network that learns to steer a car from training examples has been developed by Neusser using small feedforward ANNs [221]. The use of Adaptive Resonance Theory to learn lateral motion control was reported by Lubin [222]. Good survey papers and reference material on ANNs with FLC can be found in [219,2232301. For a comprehensive discussion of references and applications of neural networks to the control of nonlinear systems we refer the readers to [231-2331. Some of the key issues that remain are stability, validation, and verification of hybrid systems resulting from incorporation of the above modules into AVCS [234]. In the past, these issues have been addressed through the extensive use of simulations; more recently, mathematical analysis has been used to inves#tigate the stability, observability, and controllability of closed loop systems with neurocontrollers. Used in conjunction with classical synthesis methodologies, ANNs and FLCs hold great promse for the future Iof AVCS.

SOCIOECONOMIC

ISSUES

No discussion of technology is complete without taking a peek at its social, economic:, and political implications. For example, what social problems does it solve, how will it affect our way of life, and what new governmental policies will be required for deployment? The socioeconomic impacts of our present transportation system in terms of wasted work hour potential, wasted fuel and increased pollution are high. Traffic congestion in Bangkok, as an example, wastes US$500 million worth of fuel by vehicles idling in traffic [235,236]. The p-remise of ITS in America and other parallel programs throughout the world is that this congestion problem can be reduced by investing in a technological solution rather than building more roads, or effecting a social change. Everyone agrees to the benefits of reduced congestion to our socioeconomic infrastructure; however, there are differences in how various people feei such reductions can and should be effected. The various approaches warrant more study as the investm,ent in any approach is high and the payoff is far from immediate. For instance, the U.S. plans to spend US$200B on the technology approach, with payoffs reaching out in the early 21st century; Japan has already spent close to US$2B on ITS deployment [237]. W. H. von Aulock [238] suggests that it might be more beneficial to seek social change to address the congestion problem rather than assume that by commitment of huge technical resources that today’s problems will be solved.

A. GARCIA-ORTIZet al.

70

Indeed, he agrees as to whether the assumption that “progress in controls, communications and computers has made technical solutions practical for the problem of highway congestion” is reasonable, and whether any technical solution to the bigger problem of congestion is feasible. As an alternative, he argues that a social change would be more beneficial, and that with the right mix of “cars, vans and buses, it should be possible to double or triple the people moving capacity of a highway.” His arguments certainly have merit; however, social change is itself expensive, and also requires a passage of time to transition from one set of social behaviors to another of set of social norms. Arnott and Small, in their “Economics of Traffic Congestion” [239] clearly underscore that the heretofore policy of “build our way” out of the metropolitan congestion issue is doomed to failure because of three traffic paradoxes, namely (1) the Pigou-Knight-Downs (2) the Downs-Thomson

paradox of expanding road capacity with no change in congestion,

paradox of latent demand, and

(3) the mispricing of congestion in terms of private cost rather than social cost. In their paper, they provide a rule of thumb calculation method to conservatively estimate the annual cost of urban congestion at US$48 billion annually at a private cost of US$640 per driver annually. As they so clearly point out, there is a difference between the private cost to the individual of congestion and the social cost. They introduce the concept of externalities or congestion pricing. Certain toll roads have introduced this concept that urban capacity goes up so the toll price goes up. This impacts the private cost to the driver and will divert him off that road, thereby reducing capacity. The difficulty is in implementing this concept in a free society of people of free will traveling on nontoll roads. Sussman provides similar order figures for urban freeway delays in terms of a lost two billion vehicle hours per year; however, he provides that added dimension of the cost of safety [240]. The stated economic loss from traffic crashes is 2 percent of the U.S. gross national product or US$.O5 per vehicle mile traveled. This amounts to another US$SOOper year per motor vehicle in addition to the already established US$640 annually as the cost for vehicle delays due to congestion. What appears to the authors as a gap in our present studies which can adequately study and truly reflect the cost/benefits of various solutions to the present congestion problems. These cost/benefits need to consider the following: (1) (2) (3) (4) (5) (6)

cost of cost of cost of cost of cost of impact

delays due to road capacity limitations, disruption from the unpredictability of traffic delays, inconvenience to schedules due to both (I) and (21, extra fuel consumption due to (1) and (2), accidents, and of air pollution due to (1) and (2).

That does not mean there is a complete void; indeed there is a study of air pollution impacts of ATT (Advanced Transport Telematics) in Southampton, U.K. and Cologne, Germany [241] which uses an air pollution prediction model (UROPOL-Urban Road Pollution), Figure 20, to assess the impact of proposed changes such as urban traffic control (UTC) using SCOOT (Split Cycle Time and Offset Optimization Technique) and model changes to public transport. Such studies [235,239] provide the impact of change on pollution and air quality; where knowledge void exists, however, is in the prediction of behavioral responses to the public (1) to the introduction of ITS technologies, (2) to social change, and (3) to other seemingly nonrelated technology change such as the developments from the information age. Such gaps are large and uncertain. It is clear that this area is ripe for level headed research. Furthermore, a generic model as suggested by Figure 21 is required to be developed to give guidance as to the cost effectiveness of investments, and the payoff in terms of reduced congestion, and also the savings in energy, noise and emission reduction-in essence, a cost effectiveness analysis model.

Enabling Technologies ACCELERATION/ DECELERATION DATA

-

QUEING MODEL

-

71

?z:’

LINK QUERIES

VEHICLE EMISSION RISK

LINK EMISSION DISTRIBUTION

METEREOLOGICAL DATA

-

DISPEkION MODEL

+

RECEPTOR AND NETWORK GEOMETRY

POLLUTANT CONCENTRATIONS

Figure 20. Components

and data flow of the urban road pollution model (UROPOL)

Claen Cars. etc.

Pollution as a function of Schedule

Other Social Changes

Figure 21. Generic macro socioeconomic

model.

ITS was born out of a premise of a growing population, in particular, a growing suburban population. Projections of population growth stem from historical perspectives. Similarly, projections of work related travel are based upon today’s work ethics, and in the U.S. in particular on today’s culture with regard to transportation between the home and the office. It is little wonder that the future appears so daunting when viewed from this perspective. Figure 22 show the changes in summary demographic and travel patterns from 1969 to 1977 to 1983 to 1990 [242]. It should be noted that whereas the number of persons traveling normalized to the 1969 figure increased by 12 percent by 1990, the number of vehicles carrying these people increased by 128 percent. Clearly there are less people per vehicle. This is a bad social trend from our macro model perspective. The dramatic increase of drivers since 1980 relates also to the social change of the increased number of women in the workplace, and the dramatic change in vehicles also relates to the fact that families have two or more vehicles to accommodate husband and wife traveling indepen-

A. GARCIA-ORTIZ

72

et al.

dently to the workplace. When this is coupled to the statistic that the work travel length, see Figure 23, has gone up in conjunction with the number of vehicles increasing, a daunting picture emerges of increased pollution, especially central city pollution levels. When viewed from such a socioeconomic

perspective, the arguments for ITS technologies are persuasive even to the level

of using some strong demand management measures to regulate traffic. Access to the highway could be projected to be rationed to an individual on the basis of an economic measure.

Households

Persons

Figure

Drivers

22. Changes

Vehiclts

Vehicle Trips

in demographic

and travel

Figure

23. Work

Person Trips

1977

fl

1983

q

1990

Person Miles

patterns.

Non-MSA

Suburbs

Central Cities

Vehicle Miles

I

trip travel

length.

Projecting the number of vehicles in Figure 22 into the 21St century without building new highways, or changing the basic capacity of our roads, would lead to a conclusion that the road capacity could not meet these future demands. When this is coupled with the notion that here in the United States the population is aging, and has a tendency to demand increased mobility, the arguments for action become persuasive. However, whether these projections will ever materialize could depend as much on social change as outside the normal studies of ITS.

Enabling Technologies

Yet if we step back and consider

some of the changes

73

in our social order that

might

effect the

need for high levels of urban transportation, then the persuasive arguments for ITS technologies become less so even if we project a continued population growth. Perhaps the biggest impacting technology

c:ould be the Information

Highway.

Much of today’s

business

is conducted

in a manner

parallel to the way businesses conducted themselves in Victorian times, when industry went away from being a cottage industry to a centralized base. Most industries, especially white collar based industries, rely on people coming together for data exchange at a centralized location for a consistent set of eight hours, arriving at the same time every morning and leaving at the same time every afternoon. For companies to interact, a consistent set of circumstances occur at the macro level. The result is the “rush hour,” a period of high peak load capacity on our roads, and a period

of high probability

of accidents,

delays and increased

pollution.

The information age could have serious impacts on the premise fut,ure will be an extension of today’s company. Video conferencing spatially

separated

work groups

to interact

meaningfully

without

that the company of the now makes it possible for the expense

of the physical

Virtual conferencing will add to the transparency of the interaction. The advent of travel. net,working of computers, and the availability of inexpensive, powerful computers, will obviate the need for a large portion of the workforce to ever have to “come to work” to fulfill their labors. In fact, the economics of having a distributed labor force without the need for travel to and from their place of employment and without the ancillary need for business travel to and from their customer/vendor base are far reaching. Not only will the impact on the “rush hour” be significant, but there will be an impact on housing, housing location, and central office building location. Such a social change in our way of life might well solve some of the central issues being addressed by ITS without any corresponding need to develop ITS specific technologies. In a similar manner, a change in the U.S. culture of “taking the car” coupled with a building of the infrastructure of a quick, easy, reliable, inexpensive public transport system, especially around urban areas, might well have similar impacts as the rather drastic change in the way business operates today already described. it would be inappropriate to draw conclusions from such speculation; however, it would seem to the authors that there is a tremendous opportunity for further study of the socioeconomic impacts as well as associated costs for the impact of ITS technology on the future transportation system. Such a study should not exclude social and culture changes from technologies outside ITS.

REFERENCES 1. E.R. Tufte, The Vzsual Display of Quantitative Information, Graphic Press, Connecticut, (1983). 2. J.C. Antenucci et al., Geographic Information Systems, A Guade to the Technology, Van Nostrand Reinhold, New York, (1991).

3. A.K. Sen and P.V. Thakuriah, Estimation of static travel times in a dynamic route guidance system, .Wathl. Comput.

Modelling (this issue).

1. L. Skinner, On the virtual road: No more maps in laps, Washington Technology (June 23), lo-12 (1!194). 5. C.W.H. Goodwin and J.W. Lau, Vehicle navigation and map quality, In Proceedings of the Vehicle Nawigatzon ami Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 17-20. H. Claussen, Status and directions of digital map databases in Europe, In Proceedings of the Vehicle Navzgation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 25-28. 7 S. Masa.0 and Y. Fujita, Current status and future plans for digital map databases in Japan, In Proceedings of the Vehzcle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15. 1993, pp. 29-.33. 8. F.S. Budnick et al., Principles of Operations Research for Management, Irwin, Illinois, (1988). !I. T.H. Cormen et al., Introduction to Algorithms, MIT Press, Massachusetts, (1990). 10. E.F. Codd, A relational model for large shared data bases, Commzlnicatzons of the ACM 13 (6), 377-387 (June 1970). Il. S.B. Yao, Editor, Principles of Database Design, Volume 1, Logzcal Organzzations, Prentice Hall, New Jersey, (1985). 12. J.A. Bondy and U.S.R. Murty, Graph Theory with Applications, North-Holland, New York, (1976).

6

74

A. GARCIA-ORTIZ et al.

13. T. Barbagallo, Intelligent middleware: Data overload relief, AI Expert (September 1994). 14. R.E. Fenton, IVHS/AHS: Driving into the future, IEEE i?unsactions on Control Systems (December 1994). 15. M. Bodson, Emerging technologies in control engineering, IEEE tinsactions on Control Systems (Decerriber 1994). 16. R.K. Jurgen, Editor, Automotive Electronics Handbook, McGraw-Hill, New York, (1995). 17. R.L. French, Automotive Electronics Handbook, McGraw-Hill, New York, (1995). 18. D.P. Bertsekas, Linear Network Optimization, p. 359, The MIT Press, Cambridge, MA, (1991). 19. J.R. Evans and E. Minieka, Optimization Algorithms for Networks and Graphs, 2”d edition, p. 470, Marcel Dekker, New York, (1992). 20. N. Deo and C. Pang, Shortest-path algorithms: Taxonomy and annotations, Networks 14, 275-323 (1984). 21. S.E. Dreyfus, An appraisal of some shortest-path algorithms, Operations Research 17, 395-412 (1969). 22. G.S. Gallo and S. Pallotino, Shortest-path methods: A unified approach, Mathematical Programming Study 26, 38-64 (1986). 23. B. Golden and T.L. Magnati, Deterministic network optimization: A bibliography, Networks 7, 149-183 (1977). 24. S. Pallottino, Shortest-path methods: Complexity, interrelations and new propositions, Networks 14, 257-267 (1984). p. 172, 25. A. Bundy, Editor, Catalogue of Artificial Intelligence Tools, 2 nd edition, Symbolic Computation, Springer-Verlag, Berlin, (1986). 26. L. Kanal and V. Kumar, Editors, Symbolic computation, In Search in Artificial Intelligence, p. 492, Springer-Verlag, New York, (1988). 27. R.E. Korf, Depth-first iterative-deepening: An optimal admissible tree search, AZ 27, 97-109 (1985). 28. R.E. Korf, Iterative-deepening-A: An optimal admissible tree search, In Proceedings of the gth Int. Joint Conf. on AI, Los Angeles, 1985, pp. 1034-1036, Morgan Kaufman. 29. R.E. Korf, Learning to Solve Problems by Searching for Marco-Operators, Pitman, Boston, (1985). 30. R.E. Korf, Mare-operators: A weak method for learning, AI 26, 35-77 (1985). 31. R.E. Korf, Planning as search: A quantitative approach, AI 33, 65-88 (1987). 32. R.E. Korf, Optimal path-finding algorithms, In Search in Artificial Intelligence, pp. 223-267, Springer-Verlag, New York, (1988). 33. R.E. Korf, Real-time heuristic search, AI 42, 189-212 (March 1990). 34. H. Schildt, Artijicial Intelligence Casing C, p. 424, Osborne, McGraw-Hill, Berkeley, CA, (1987). 35. B.S. Stewart, C.-F. Liaw and C.C. White, A bibliography of heuristic search research through 1992, IEEE Trans. Systems, Man, and Cybernetics 24 (2), 268-293 (1994). 36. B.S. Stewart and C.C. White, Three solution procedures for multiobjective path problems*, Control: Theory and Advanced Technology 5, 443-470 (December 1989). 37. B.S. Stewart and C.C. White, Multiobjective A*, J. of ACM 38, 775-814 (October 1991). 38. S.L. Tanimoto, The Elements of Artificial Intelligence, p. 552, Computer Science Press, Rockville, MD, (1987). 39. R. Sedgewick, Algorithms in C, p. 674, Addison-Wesley, (1990). 40. C.H. Papadimitriou and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity, p. 519, Prentice-Hall, Englewood Cliffs, NJ, (1982). 41. 42. 43. 44. 45. 46. 47. 48. 49. 50.

51. 52. 53.

M.M. Solomon and J. Desrosiers, Time window constrained routing and scheduling problems: A survey, Transportation Science 22 (l), 1-13 (1988). F. Glover, D. Klingman, N. Phillips and R. Schneider, New polynomial shortest path algorithms and the computational attributes, Manage. Sci. 1, 1106-1128 (1985). F. Glover, D. Klingman and N. Phillips, A new polynomially bounded shortest path algorithm, Operations Research 33, 65-73 (1985). M. Hung and J.J. Divoky, A computational study of efficient shortest path algorithms, Comput. Oper. Res. 15, 567-576 (1988). R. Dechter and J. Pearl, Generalized best-first search analysis and the optimality of A*, J. of ACM 32, 505-536 (July 1985). R. Dechter and J. Pearl, The optimality of A*, Search in Artificial Intelligence, pp. 166-199, Springer-Verlag, New York, (1988). P.E. Hart, N.J. Nilson and B. Raphael, A formal basis for the heuristic determination of minimum cost paths, IEEE IPrans. on Systems Science and Cyber. 4, lo&107 (1968). P.E. Hart et al., SIGART Newsletter, Vol. 37, pp. 28-29. T.A. Nicholson, Finding the shortest route between two points in a network, Comput. J. 9, 275-280 (1966). R.V. Helgsson, J.L. Kennington and B.D. Stewart, Dijkstra’s two-tree shortest path algorithm, Technical Report 88-OR-13, Dept. of Operations Research and Engineering Management, Southern Methodist University, Dallas, Rev., (July 1988). G.S. Gallo and S. Pallottino, Shortest path algorithms, Annals of Operations Research 7, 3-79 (1988). R. David and H. Alla, Petri Nets for modeling of dynamic systems: A survey, Automatica 30 (2), 175-202 (1994). R. Dial, F. Glover, D. Karney and D. Klingman, A computational analysis of alternative algorithms and labeling techniques for finding shortest path trees, Networks 9, 215-248 (1979).

Enabling Technologies

75

54. G.N. Frederickson, Distributed shortest path algorithms for a planar network, Information and Computation 86 (2):x40-159 (1990). 55. J.J. Garcia-Luna-Aceves, Distributed routing with labeled distances, In Proceedings of the 1 lth Annual Conference of the IEEE Computer and Communications Societies, Florence, Italy, 1992, pp. 633-643. 56. S. Goto, T. Ohtsuki and T. Yohimura, Sparse matrix techniques for the shortest path problem, IEEE Trans. Cwcuits and Systems 23, 752-758 (1976). 57. Bi. Jiang, I/O-efficiency of shortest path algorithms: An analysis. In Proceedings of the Xth IEEE InTernational Conference on Data Engineering, 1992, pp. 12-19. 58. L. Kleinrock and F. Kamoun, Hierarchical routing for large networks, Computer Networks 1, 155-174 (1977). 59. J.B.H. Kwa, BS*: An admissible bidirectional staged heuristic search algorithm, AI 38 (February). 95--109 (1989). 60. M. Minasi, Optimal searches and a challenge, AI Expert 4, 21-23 (January 1989). 61. J.J. More and S.J. Wright, Optimization Software Guide. Vol. 14 of Frontiers in Applied Mathematics, p. 154, Society for Industrial and Applied Mathematics, Philadelphia, PA, (1993). 62. A. Orda, R. Rom and M. Sidi, Minimum delay routing in stochastic networks, In Proceedings of the 1 lfh Annual Conference of the IEEE Computer and Communications Societies, Florence, Italy. 1992, pp. 608-616. 63. A. Orda and R. Rom, Shortest-path and minimum-delay algorithms in networks with time-dependent edge lengths**, J. of the ACM 37 (3), 607-625 (1990). 64. P.M. Spira, A new algorithm for finding all shortest paths in a graph of positive arcs in average time 0(n2 log2(n)), SIAM Journal on Computing 2 (March), 28-32 (1973). 65. C.C. Skiscim and B.L. Golden, Solving k-shortest and constrained shortest path problems efficiently. Annals of Operations Research 20, 249-282 (1989). 66. R.E. Tarjan, Fast algorithms for solving path problems, J. of ACM 28, 594-614 (July 1981). 67. R.E. Tarjan, A unified approach to path problems, J. of ACM 28, 577-593 (July 1981). 6X. .J. Hershberger and S. Suri, Efficient computation of Eucledian shortest paths in the plane, In Proceedings of the 1993 IEEE 34 th Annual Symposium on Fbundations of Computer Science, Palo Alto, CA, 3-5 November 1993, pp. 508-517. 69. R. Dubos, H. Margenau and C.P. Snow, Editors, The Engineer, pp. 144-169, Time Inc., New York, (1966). 70. A. Stokes, C. Wickens and K. Kite, Display Technology-Human Factors Concepts, Society of Automotive Engineers, (1990). 71. J. Malec and M. Morin, An AI-based design of a driver information unit, In Proceedings of the b’ehicle Navigation and Information Systems Conference, Ottawa. Canada, October 12-15, 1993, pp. A3-A7. 72. J.F. Collins and A. Stevens, The DRIVE-HARDIE project: Design of the man-machine interface, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 405-409. 73. W.B. Verwey, Further evidence for benefits of verba. route guidance instructions over symbolic spatial guidance instructions, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 227-231. 7.1. A.M. Parkes and G.E. Burnett, An evaluation of medium range “advanced information” in route-guidance displays for use in vehicles, In Proceedings of the Vehicle Navigatzon and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 238-241. 75. T. Obata, T. Daimon and H. Kawsshima, A cognitive study of in-vehicle navigation systems: Applying verbal protocol analysis to usability evaluation, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12--15, 1993, pp. 232-237. 76. G.E. Burnett and S.M. Joyner, An investigation on the man machine interface to existing route guidance systems, In Proceedings of the Vehicle Navigation and Informatzon Systems Conference. Ottawa, Canada, October 12-15, 1993, pp. 395-400. 77. A. Spoerri, Novel route guidance displays, In Proceedings of the Vehicle Navigation and InJormafton Systems Confennce, Ottawa, Canada, October 12-15, 1993, pp. 419-422. 78. S.E. Underwood and S.G. Gehring, Approaches to IVHS evaluation: Advanced traveler information systems and advanced traffic management systems, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12.-15, 1993, pp. 247-250. 79. hl. Ness and M. Herbert, A prototype low cost in-vehicle navigation system, In Proceedings of the 1rehzcle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 56-59. 80. R.N. Fleischman, L. Thelen and D. Dennard, A preliminary account of TravTek route guidance use by rental and loc,zl drivers, In Proceedings of the Vehicle Nwigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 12&125. 81. A. Stevens and D.K. Martell, Development and evaluation of the TrafFicMaster driver information system, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993. pp. 251-258. 82. N.D. Lerner et al., Preliminary human factor guidelines for crash avoidance warning devices, NHTSA Interim Report, November, (1983). 83. S.L. Susen and J.A. Parviainen, Advanced travel accessibility systems for the elderly and disabled: .4 conceptual framework, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada., October 12-15. 1993, pp. 375-380.

A. GARCIA-ORTIZ et al.

76

84. B. de Saint-Laurent, C. Chauvet and F. Khoda, Advanced public transport passenger information: Findings of the Eurobus project, In Proceedings of the Vehicle Navigation and Information Systems'Conference, Ottawa, Canada, October 12-15, 1993, pp. 78-81. S. Hoffman and C. Stewart, Text-based routing: An affordable way ahead?, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 45-48. 86. M.F. Land and D.N. Lee, Where we look when we steer, Native 369 (June 1994). 87. M. Hirano, Development of vehicle following distance warning system for trucks and buses, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993. 85.

88. T. Tsumura, H. Okubo and N. Komatsu, A method of position and attitude measurement of vehicle using fair shaped laser beam and corner cube, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 517-520. M. Cohn, A millimeter wave retrodirective transponder for collision/obstacle avoidance and navigation/ transponder, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 534-538. 90. M. Heller and M. Huie, Vehicle lateral guidance using vision, passive wire, and radar sensor, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 505-508. 91. C. Cugiani, L. Giubbolini and Ft. Tascone, Millimeter-wave guard rail tracking system for vehicle lateral control, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 521-524. 89.

92. 93. 94. 95.

S.A. Zolubrowski, Low cost antenna alternatives for automotive radars, Microwave Journal (July 1994). W. Zhang, Vehicle health monitoring for AVCS, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 501-504. R.K. Jurgen, The electronic motorist, IEEE Spectrum (March 1995). L.A. Klein, ‘Ikaffic parameter measurement technology evaluation, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 529-533.

96. E.L. Dagless, A.T. Ali and J. Bulas Couz, Visual road traffic monitoring and data collection, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 146-149. 97.

R.J. Blisset, C. Stennatt and R.M. Day, New techniques for digital CCTV processing in automatic traffic monitoring, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 137-140.

98.

J.J. Martinz, Evaluation through field trials of a computer vision based automatic incident detection system, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, p. 145.

J.P. White, Swords and ploughshares: The dual-use role of the global positioning system, Defense Electronics (May 1994). 100. D. Herskovitz, A sampling of global positioning system receivers, Journal of Electronic Defense (May 1994). 101. E. Krakiwsky, The diversity among IVHS navigation systems worldwide, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 433-436. 102. L. Sweeney, W.B. Zavoli and G. Loughmiller, Comparative performance of various automotive navigation technologies, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 437-440. 99.

103.

W. Kingenbert, Location of the Vehicle Navigation pp. 441-444.

104.

D. Haccoun, C. Rosenbert Navigation and Information

referencing systems for dynamic route guidance applications, In Proceedings and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, and M. Caron, Radiopositioning by satellite, In Proceedings of the Vehicle Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 449-455.

105. S. Terry and M. Dunbar, Tradeoffs in silicon accelerometer

design, Sensors (August

106.

R. Leonardson and D. MacGugan, (August 1994).

of a commercial

107.

G. Waldman et al., A normalized Processing 42, 137-156 (1988).

108.

K. Luetkemeyer et al., Evaluation of segmentation techniques applied to prescreened areas of multisensor imagery, In Proceedings of the National Aerospace and Electronics Conference, Dayton, OH, May 1986, Vol. 1, pp. 197-204.

Design and fabrication clutter

measure for images,

Computer

1994).

trisxial accelerometer, Vision,

Graphics,

Sensors

and Image

109. J. Keller et at., Target recognition using the Karhunen-Loeve transform, In Proceedings of the International Conference on Systems, Man and Cybernetics, Tucson, AZ, November 1985, pp. 31&314. 110. J. Keller et al., Fuzzy confidence measures in multitemporal imagery, IEEE Applied Imagery Pattern Recognition Workshop, Baltimore, MD, October 1985. 111. J. Wootton et al., The use of fuzzy set theory to build confidence measures in multisensor imagery, IEEE Applied Imagery Pattern Recognition Workshop, Baltimore, MD, October 1985. 112. J. Keller et al., Fuzzy confidence measures in midlevel vision, IEEE Transactions on Systems, Man and Cybernetics SMC-17 (4), 676-683 (1987).

77

Enabling Technologies

J. Wootton et al., A multiple hypothesis rule-based automatic target recognizer, In Pattern Recognition, Lecture Notes in Computer Science, Vol. 301, (Edited by J. Kettler), pp. 315-324, Springer-Verlag, Berlin, (1988). 114. G. Waldman and J. Wootton, Electra Optical Systems Performance Modelling, CA 2, Artech House, (1993). 115. P.R. Norton, Infrared image sensors, Opt. Eng. 30 (ll), 1649-1663 (November 1991). Spectra, 86-92 (July 1993). 116. H. Kaplan, A new world for IR technology, Photon& 113.

J. Bond, Changing technologies point IR imaging to real world problems, Photon& Spectra, 95-100 (July 1993). 118. N. El-Sheimy and K.P. Schwartz, Kinematic positioning in three dimensions using CCD technology, In PTOceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 472-475.

117.

119.

D.N. Green et al., Guidance and control of an autonomous planetary rover, In Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993, pp. 539-546.

120. IVHS architecture development program, Interim Status Report, Washington, DC, (April 1994). 121. ITS architecture development program, Phase 1 Summary Report, Washington, DC, (November 1994). Intelligent vehicle highway systems, Review of field trials, IRRD No. 847491, Organisation Co-operation and Development, Paris, (1992). 123. Wireless messaging: Understanding the technology, Intel Corporation, (1993). 122.

124. M. Wagg, MOBILESAT, Australia’s own, In Proceedings of the Second International ference, Ottawa, Ontario, Canada, June 17-20, 1990, pp. 3-7. 125.

For economic

Mobile Satellite Con-

H. Haugli, Implementation of inmarsat mobile satcom systems, In Proceedings of the Second International Mobile Satellite Conference, Ottawa, Ontario, Canada, June 17-20, 1990. pp. 8-12.

126. M.A. Carlock, Incorporation of radio transponders into vehicular on-board diagnostic systems, In Proceedings of the 1994 International Congress on nansportation Electronics. Dearborn, MI, October 17-19. 1994, pp. 111-114. 127.

I.M. Jacobs et al., A second anniversary operational review of the OmniTRACS-The first two-way mobile Ku-band satellite communications system, In Proceedmgs of the Second Internatzonal Mobile Satellzte Conference, Ottawa, Ontario, Canada, June 17-20, 1990, pp. 13-18.

128. RAM mobile data system overview, Report No. RMDUS 031-RbIDSO-RM, Release 4.1. RAhl Mobile Data, Woodbridge, NJ, (September 30, 1993). 129. J. Martin, Telecommunications and the Computer, 3*d edition, Prentice Hall, New Jersey, (1990). 130. L. Marion, Ubiquitous mobile communications, Electronic Business Buyer, 46-49 (February 1995). 131.

T. Sexton, Cellular communication: Its role in personal protection and vehicle safety, In Proceedings of the 1994 International Congress on Trunsportation Electronics, Dearborn, hl1, October 17-19, 1994, pp. 521-523.

132.

K. Gardels, Automatic car controls for electric highways, GM Research Laboratories, Michigan, (June 1960).

133.

B.W. Stephens, D.A. Rosen, F.J. Mammano and W.L. Gibbs, Third generation destination signaling: electronic route guidance system, Route Guidance, Highway Research Record No. 265, (1968).

GMR-276.

Warren. An

134. D. Rosen, F.J. Mammano and R. Favout, An electronic route-guidance systems for highway vehicles, IEEE Transaction on Vehicular Technology VT-19 (l), 143-152 (1970). 135. N. Yumoto, H. Ihara, T. Tabe and M. Naniwada, Outline of the CACS pilot test system. presented at the 5gth Transportation Research Board Annual Meeting, (1979). 136. P. Braegas, Function, equipment, and field testing of route guidance and information system for drivers (ALI), IEEE Trunsactions on Vehicular Technology VT-29 (2), 216-225 (1980). 137. P. Glathe, L. Karlsson, G.P. Brusaglino and L. Calandrino, The Prometheus Programme--ObJectives, concepts, and technology for future road traffic, FISITA Paper 905180, pp. 477-484, (1990). 138. J. Wootton, A. Garcia-Ortiz and S.M. Amm, Intelligent transportation systems: A global perspective, Mathl. Comput. ModeEling (this issue). 139. G. Ofstead, Advanced traffic management activities at IVHS America, In Proceedings of CONVERGENCE ‘92-The 1992 International Congress on Transportatzon Electronics, Dearborn, MI, 19-21 October 1992, pp. 409-412. 140. Intelligent Vehicle-Highway Systems-Course 2328, University of Michigan, July 26-29, 1993. 141. M. Gaudry, Responsibility for accidents: Relevant results selected from the DRAG model, paper prepared for the Seventeenth Annual Workshop on Commercial and Consumer Law, University of Toronto, Canada, October 1987. 142. M. Gaudry, An outline of the DRAG comprehensive road safety model, Bxnsportation In Canada, Transport Canada TP 10451-E, Ottawa, 53-59 (1990). 143. W.C. Collier and R.J. Weiland, Smart cars, smart highways, IEEE Spectmm, 27-33 (April 1994). 144. P.R. Lowrie, The Sydney coordinated adaptive traffic system: Principles, methodology, algorithms, Int’l Conference on Road Tmfic Signaling, IEE Pub. No. 207, pp. 67-70, (1982). 145. J.Y.K. Luk, A.G. Sims and P.R. Lowrie, SCATS-Application and Field Comparison with a TRANSYT Optimised Fixed-Time System, In Proceedings of the First Int’l Conf. on Road Trafic Signaling, IEE Pub. No. 207, pp. 71-74, (1982).

78 146.

147. 148. 149. 150.

151. 152.

153.

154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170.

171. 172. 173. 174.

175.

A. GARCIA-ORTIZ et al. D.E. Boyce, J.L. Schafer and A.M. Kirson, Scope, feasibility and cost of a dynamic route guidance system demonstration, Report prepared for the Illinois DOT by the Univ. of Illinois at Chicago, Northwestern University, and Motorola, (August 1990). DRIVE Conference Proceedings, Advanced Telematics in Road Transport, Two volumes, Elsevier, (1991). S.E. Underwood, Modeling-ATIS and ATMS, In Proceedings of the University of Michigan’s Engineering Summer Conference on ZVHS, (July 27, 1993). M. Ben-Akiva, H.N. Koutsopoulos and A. Mukundan, A dynamic traf6c model system, Presented at the Second International Seminar on Urban ‘Bnfic Networks, Capri, Italy, (July 1992). D.E. Boyce, A.M. Kirson and J.L. Schafer, Chapter 11: ADVANCE: The Illinois dynamic navigation and route guidance demonstration program, In Advanced Technology in Transport: IVHS and ATT, (Edited by I. Catling), Artech House, London, (December 1992) (to appear). G.-L. Chang, T. Junchaya and A.J. Santiago, A real-time network traffic simulation model for ATMS applications, IVHS Journal 1 (3), 227-241 (1994). R.M. Ingle, B.D. Williams and N. Sobhi, An advanced traffic management system simulator for intelligent vehicle-highway systems research, In Proceedings of 1994 Winter Simulation Conference, Lake Buena Vista, FL, 11-14 December 1994, (Edited by Tew, Manivannan, Sadowski, and Seila), pp. 1455-1460. D. Ramsswamy, J.V. Medanic, W.R. Perkins and R. Benekohal, Lane assignment on an automated highway, In Proceedings of the 1994 American Control Conference, Baltimore, MD, 29 June-l July 1994, Vol. 1, pp. 413-17. K. Vaughn, M. Abdel-Aty, R Kitamura, P. Jovanis and H. Yang, Experimental analysis and modeling of sequential route choice behavior under ATIS in a simplistic traffic network, Research Report, (1992). D.J. Bertsimas and G. Van Ryzin, A stochastic and dynamic vehicle routing problem in the Euclidean plane, Operations Res. 39 (4), 601-615 (July-August 1991). D.J. Bertsimas, A vehicle routing problem with stochastic demand, Operations Res. 40 (3), 574-585 (May-June 1992). G.A. Davis and N.L. Nihan, Large population approximations of a general stochastic traffic assignment model, Operations Res. 41 (l), 169-178 (1993). M. Dror, G. Laporte and P. Trudeau, Vehicle routing with stochastic demands: Properties and solution framework, Transportation Science 23 (3), 166-176 (August 1989). R.E. Bixby, J.W. Gregory, I.J. Lustig, R.E. Marsten and D.F. Shanno, Very large-scale LP: A case study in combining interior point and simplex methods, Operations Research 40 (5), 885-897 (1992). R.E. Bixby, Implementing the simplex method: The initial basis, ORSA Journal on Computing 4 (3), 267-284 (1992). D.J. Bertsimas and G. van Ryzin, A stochastic and dynamic VR problem in the Euclidean plane, Operations Res. 39 (4), 601-615 (1991). D.J. Bertsimas and G. van Ryzin, Stochastic and dynamic VR in the Euclidean plane with multiple capacitated vehicles, Operations Res. 41 (l), 60-76 (1993). L.S. Lasdon and S. Luo, Computational experiments with a system optimal dynamic traffic assignment model, Technical Paper, University of Texas-Austin, (1993). J.R. Birge and J.K. Ho, Optimal flows in stochastic dynamic networks with congestion, Operations Res. 41 (l), 203-216 (1993). M. Carey and A. Srinivasan, Externalities, average and marginal costs, and tolls on congested networks with time-varying flows, Operations Res. 41 (l), 217-231 (1993). T.L. Friesz, J. Luque, R.L. Tobin and B. Wie, Dynamic network traffic assignment considered as a continuous time optimal control problem, Operations Res. 37 (6), 893-901 (1989). M. Papageorgiou, Dynamic modeling, assignment, and route guidance in traffic networks, Transportation Research-B 24B (6), 471-495 (1990). M. Papageorgiou, J.C. Moreno-Banos and A. Messmer, Optimal control of multidestination traffic networks, In Proceedings of the 2gth Conference on Decision and Control, Hawaii, pp. 13551361, December 1990. J.M. Morin, P. Gower, M. Papageorgiou and A. Messmer, Motorway networks, modelling and control, In Proceedings of the DRIVE Conference, Vol. I, Brussels, 4-6 February 1991, pp. 148-171, Elsevier. J.M. Morin, M. Papageorgiou, H. Haj-Salem, J.C. Pierrelee and J.F. Gabard, Validation results of traffic flow modelling on linear motorways, In Proceedings of the DRIVE Conference, Vol. II, pp. 981-1006, Brussels, 4-6 February 1991, Elsevier. A. Messmer and M. Papageorgiou, Route diversion control in Motorway networks via nonlinear optimization, IEEE Transactions on Control Systems Technology 3 (l), 144-154 (March 1995). D. Rock, D. Huskiness and D. Malkoff, Intelligent road transit: The next generation, AI Ezpert 9 (4), 16,18-24 (April 1994). C. Chen, R.V. Neppalli and N. Aljaber, An effective heuristic for continuous flow shop problems, Technical paper, MS State University, (1993). J.F. Gilmore, K.J. Elibiary and R.J. Peterson, A neural network approach to special event traffic management, Paper presented at the National Conference on Advanced Technologies in Public Transportation. San Francisco, CA, August 1992. B.G. Heydecker and J. Wu, A knowledge-based system for road accident remedial work, Paper presented at the Second International Conference on the Application of AI Techniques to Civil and Structural Engineering, Oxford, England, September 1991.

Enabling Technologies

79

176. J.D. Leonard, B. Ramanathan and W.W. Reeker, A real-time information processing algorithm for the evaluation and implementation of ATMS strategies, In Proceedings of the Intelligent Vehicles ‘92 Symposium, Detroit, MI, 29 June-l July 1992, pp. 225-229. 177. J.S. Albus, M. Juberts and S. Szabo, A reference model architecture for intelligent vehicle and highway systems, In Proceedings of the Intelligent Vehicles ‘92 Symposium, Detroit, MI, 29 June-l July 1992, pp. 378-384. 178. P. Variaya, Smart cars on smart roads: Problems of control, IEEE ‘Pran. Automatic Control 38 (2), 195-207 (February 1993). 179. A. Garcia-Ortiz, A semantic control approach to counter-measures management, In the Proceedings of the 1992 Canadian Conference on Electrical and Computer Engineering, Toronto, Canada, September 13-16, 1992. 180. A. Garcia-Ortia et al., Application of semantic control to a class of pursuer-evader problems, Computers Math. Applic. 26 (6), 97-124 (1993). 181. A. Garcia-Ortiz, Adaptive vehicle navigation in hostile environments, In the Proceedings of the 1993 Canadian Conference on Electrical and Computer Engineering, Vancouver, Canada, September 14-17, 1993. Tactical situation management using semantic control, In the Proceedings of the First 182. A. Garcia-Ortiz, World Congress on Intelligent Manufacturing, Mayaguez, Puerto Rico, February 13-15, 1995. 183. E.Y. Rodin, Semantic control theory, Appl. Math. Lett. 1 (l), 73-78 (1988). 184. Y. Lirov, Artificial intelligence methods in decision and control systems, Doctoral Thesis, Washington University, St. Louis, MO, (August 1987). 185. S.M. Amin, Intelligent prediction methodologies in the navigation of autonomous vehicles, Doctoral Thesis, Washington University, St. Louis, MO, (January 1990). 186. R.D. Weil, AI methods in utilizing low dimensional models of differential games, Doctoral Thesis, Washington University, St. Louis, MO, (September 1990). 187. D. Geist, Semantic control in continuous systems applications to aerospace problems, Doctoral Thesis, Washington University, St. Louis, MO, (December 1990). 188. F. Yang, Network optimization with time window constrained routing and scheduling, Doctoral Thesis, Washington University, St. Louis, MO, (August 1995). 189. E.Y. Rodin et al., Intelligent routing & scheduling project, Center for Optimization and Semantic Control, Washington University in St. Louis, (1993). 190. L.E. Sweeney and W.B. Zavoli, Trends in digital road map databases for microcomputer applications in transportation, In Microcomputer in Transportation, Proceedings of the dth International Conference, Baltimore, MD, 22-24 July 1992, (Edited by Chow, Litvin and Opiela), pp. 257-267. 191. J. de D. Ortuzar and A.J. Daly, Forecasting and data aggregation: Theory and practice, ‘Z+afic Engineering and Control 31 (2), 632-643 (1990). 192. J. de D. Ortuzar and L.G. Willumsen, Monitoring of transport plans using low cost data and advanced modeling techniques, In Simplified lPransport Demand Modelling, Perspectives 2, pp. 121-131, PTRC, London, (1992). 193. B. Neenan and G.P.Y. Huang, Information requirements for an integrated transit/traffic management and traveler information system, ZVHS Journal 1 (2), 167-180 (1993). 194. M. Choy, M.-P. Kwan and Hong Va Leong, On real-time distributed geographical database systems, In Proceedings of the Twenty-Seventh Annual Hawaii International Conference on System Sciences, Wailea, HI, 4-7 Jan. 1994, Vol. IV, (Edited by J.F. Nunamaker, Jr. and R.H. Sprague, Jr.), pp. 337-346. 195. K. Laraqui, M. Lengdell, F. Reichert and A. Fasbender, Implementation of communication and management protocols for the integrated road transport and traffic environment, In Proceedings of IEEE 44th Vehicular Technology Conference (VT), Stockholm, Sweden, S-10 June 1994, Vol. 1, pp. 379-383. 196. R. Arnott, A. de Palma and R. Lindsey, Does providing information to drivers reduce traffic congestion?, ‘Ilansportation Research A 25A (5), 309-318 (1990). 197. M.J. Kelly and D.J. Folds, Intelligent vehicle-highway systems (IVHS): The ‘I’ in IVHS is still mostly human intelligence, In Proceedings of the 1994 IEEE National Telesystems Conference NTC-94, San Diego, CA, 26-28 May 1994, (Edited by M.I. Hoffman), pp. 235,266. 198. S.E. Shlsdover, Research and development needs for advanced vehicle control systems, IEEE M~CTO13 (l), 11-19 (February 1993). 199. Y. Oshima et al., Control system for automatic automobile driving, In Proceedings of ZFAC Tokyo Symposium on Systems Engineering for Control Systems, pp. 345-357, (1965). 200. K.H.F. Cardew, The automatic steering of vehicles-An experimental system fitted to a DS Citroen Car, Report RRL-LR-340, Road Research Laboratory, Crowthorne, England, (1970). 201. R. Fenton et al., An investigation of highway automation, Reports EE276A-6, (1969), and EES-276A-15, (1974), Ohio State University. 202. IEEE ‘Transactions on Vehicular Technology VT-19 (1) (February 1970). IEEE tinsactions on Vehicular Technology VT-28 (1) (February 1979). 204. IEEE tinsactions on Vehicular Technology VT-40 (1) (February 1991). J. Ackermann, Problems of individual transportation: From a two-mass model to a one-mass model of a car steering, In Proceedings of the Znt’l Symp. on Systems and Networks: Mathematical Theory and Applications, Regensburg, Germany, Aug. 2-6, 1993, (Edited by U. Helmke, R. Mennicken and J. Saurer), pp. 9-27.

80 206. 207.

208. 209. 210. 211. 212.

213. 214. 215.

216.

217. 218. 219. 220. 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234.

235.

236. 237. 238. 239.

A. GARCIA-ORTIZ et al. E. Ono et al., Vehicle integrated control for steering and traction systems by m-synthesis, Automatica 30 (ll), 1689-1647 (November 1994). S.E. Shladover, The California PATH program: A state approach to IVHS research, In Proceedings of CONVERGENCE ‘92-The 1992 International Congress on hnsportation Electronics, Dearborn, Ml, 19-21 October 1992, pp. 329-38. P.P. Varaiya, Sketch of an IVHS systems architecture, PATH research report, UCB-ITS-PRR-91-3, Rev. February 2, 1991, pp. 1055-1425. J.K. Hedrick, M. Tomiziuka and P.P. Varaiya, Control issues in automated highway system, IEEE Control Systems Magazine, 21-32 (December 1994). B.S.Y. Rae, P. Varaiya and F. Eskafi, Investigation into achievable capacity and stream stability with coordinated intelligent vehicles, tinsportation Research Record (to appear). A. Hitchcock, Intelligent vehicle/highway system safety: Multiple collisions in AHS Systems, PATH Technical Report, Inst. of Transportation Studies, UC-Berkeley, CA, (to appear). J. Ackermann, J. Guldner and V.I. Utkin, A robust nonlinear control approach to automatic path tracking of a car, presented at the International Conference on Control ‘94, Coventry, U.K., 21-24 March 1994, Vol. 1, pp. 196-201. H.Y. Chiu et al., Vehicle-follower controls with variable gains for short headway automated guideway transit systems, ASME Journal of Dynamic System, Measurement and Control 99, 183-189 (1977). S. Sheikholeslam, Control of a class of interconnected nonlinear dynamical systems: The platoon problem, Ph.D. Dissertation, UC-Berkeley, (1991). D.H. McMahon, V.K. Narendran, D. Swaroop, J.K. Hedrick, K.S. Chang and P.E. Devlin, Longitudinal vehicle controllers for IVHS: Theory and experiment, In Proceedings of the 1992 American Control Conference, Chicago, IL, 24-26 June 1992, Vol. 2, pp. 1753-1757. B. Bosacchi and I. Mssaki, Application of fuzzy logic technology (FLT) to the intelligent vehicle-highway system, In Proceedings of the SPZE-The International Society for Optical Engineering, Boston, MA, 8-10 September 1993, Vol. 2061, pp. 183-196. B. Kosko, Neural Networks and fizzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice-Hall, Englewood Cliffs, NJ, (1992). S.G. Kong and B. Kosko, Adaptive fuzzy systems for backing up a truck-and-trailer, IEEE nuns. on Neuml Networks 3 (2), 211-223 (1992). R.R. Yager and L.A. Zsdeh, Editors, fizzy Sets, Neural Networks, and Soft Computing, Van Nostrand Reinhold, New York, (1994). B. Kosko, Fuzzy systems as universal approximators, IEEE Trans. on Computers (to appear); an early version appears in Proc. lSt IEEE Conj. on Fuzzy Systems, pp. 1153-1162. S. Neusser et al., Neurocontrol for lateral vehicle guidance, IEEE Micro, 57-66 (February 1993). J.M. Lubin et al., Lateral control of an autonomous road vehicle in a simulated highway environment using adaptive resonance neural networks, Tech. Report, Dept. of CE and OR, Princeton University, (1992). D.A. White and D.A. Sofage, Editors, Handbook of Intelligent Control: Neural, Fuzzy and Adaptive Approaches, Van Nostrand Reinhold, New York, (1992). S.T. Welstead, Neural Networks and Fuzzy Logic Applications in C/C++, John Wiley and Sons, Inc., (1994). T. Terano, K. Asai and M. Sugeno, Editors, fizzy System Theory and its Applications, Translated by C.G. Aschmann, Academic Press, (1992). R.R. Yager and D.P. Filev, Essentials of fizzy Modeling and Control, John Wiley and Sons, (1994). T.J. Ross, Fuzzy Logic with Engineering Applications, McGraw-Hill, (1995). E. Cox, The Fuzzy Systems Handbook, Academic Press, (1994). J.-S.R. Jang and C.T. Sun, Neurofuzzy modeling and control, In Proceedings of IEEE, (March 1995). T. Hessberg and M. Tomizuka, Fuzzy logic control for lateral vehicle guidance, IEEE Control Systems Magazine, 55-63 (August 1994). K. Hunt et al., Neural networks for control systems-A survey, Automatica 28 (6), 1083-1112 (1992). R. Zbikowski et al., A review of advances in neural adaptive control systems, Technical report ESPRIT III Project 8039:NACT, Daimler-Benz AG and University of Glasgow, (1994). S.M. Amin et al., Application of dynamic neural networks to approximation and control of nonlinear systems, Advances in Computational Mathematics (submitted). Schultz, J.J. Grefenstette and K.A. De Jong, Adaptive testing of controllers for autonomous vehicles, In PTOC. of the 1992 Symp. on Autonomous Underwater Vehicle Technology, Washington, DC, June 1992, pp. 158-164. A.M. Khan, Energy efficiency and environmental quality through intelligent vehicle highway systems (IVHS) technologies. In the Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993. Assessment of transportation growth in Asia and its effects on energy, the environment, and traffic congestion, International Institute for Energy Convention, Washington, DC, (1993). On the virtual road: No more maps in laps, Washington Technology, (June 23, 1994). W.H. von Anlock, Smart cars may not be smart, IEEE Spectrum 31 (3), 16-18 (March 1994). R. Arnott and K. Small, The economics of traffic congestion, American Scientist 82 (September 1994).

Enabling Technologies 240.

81

J.M. Sussman and J.R. East, Intelligent vehicle highway systems: Challenge for the future. IEEE MTT-S Digest (1993). 241. P.J. Taylor and M.J.D. Herbert, Assessment of pollution impacts-ATT projects: Southampton and cologne, In the Proceedings of the Vehicle Navigation and Information Systems Conference, Ottawa, Canada, October 12-15, 1993. 242. J.D. Nystuen, Intelligent vehicle highway systems socioeconomic aspects, presented at the IVHS Szlmmer Engineering Conference, University of Michigan, July 1993.