- Email: [email protected]
GIS-Based Transportation Planning and Analysis: A Practical Implementation
Copyright © IFAC Transportation Systems Chania, Greece, 1997
GIS-BASED TRANSPORTATION PLANNING AND ANALYSIS: A PRACTICAL IMPLEMENTATION Alexandros Deloukas lIias Kokkinos Georgios Kiousis Despina Zannou
Attiko Metro A.E.
Abstract: The potential of using advanced geographic information system (GIS) technology in transportation is illustrated in the context of the Metro Development Study (MDS). Data management issues related to the conceptual design of the MDS geographical data base and the representation of public transport routes are discussed. A geocoding application serving the needs of the transportation model is described. Examples of data display (thematic maps) and data manipulation (map overlays) are given, and findings of spatial data analysis are presented. The overview of GIS-based transportation applications within MDS illustrates the advantages of integrating both systems. Keywords: GIS and transportation modelling, Conceptual design. E-R model. Digital map, Geocoding, Route system, Spatial data analysis.
1.
INTRODUCTION
The aim of this paper is, first to stress the complementarity of geographic information systems (GIS) and transportation models (TM) in general, and their specific integration within the frame of the Metro Development Study (MDS) , a comprehensive multi-modal transportation planning effort being undertaken by Attiko Metro. Second. to illustrate the structure of the large and complex MDS geographical data base. Third, to demonstrate transportation-related applications of GIS, currently underway within the MDS context. The structure of the paper is as follows :
network as well as of land uses are based. is in that context described. The following sections are organized along applications referring to basic spatial types of entities such as points. lines and areas with boundaries. In section 4. the results of geocoding of point entities. such as trip end addresses recorded during the MDS surveys are presented. The fifth section explains the use of the dynamic segmentation technique 'vvithin MDS to represent linear features, such as public transport toutes. In section 6. the results of several map transformations of MDS spatial data are displayed. The seyenth section discusses empirical findings of multivariate spatial statistics interfaced with transformations of the MDS geographical data base.
In section 2, the specific features of both systems and the way they are coupled together within the frame of MDS are described. The third section contains the conceptual design of the MDS geographical data base. The entity-relationship model on which the digital maps of road and public transport
The paper ends with some suggestions about a regular updating of the geographical data base.
417
2. GIS AND TRANSPORT MODEL INTEGRATION
Within the frame of MDS. a pragmatic approach of loosely coupling both systems (via so called "cold linkages") has been pursued. Automated linkages between GIS and TM level are provided via interface routines, which convert data tables into a format that each system can read. Data exchange has been greatly enabled by the common coding of network features at bOtll levels. The specific platforms used in the study are (a) ARCINFO (workstation version 7.01), a vector-based GIS package, (b) EtvfME/2 (release 8), a multimodal equilibrium assignment transport model package.
As a spatial information system, GIS is an appropriate platform integrating the geometry and topology of spatial objects with attribute data expressing metric or non-metric properties linked to the spatial objects. GIS can represent realistically the geometry of transportation networks, such as shape, distance and positional properties. It can also express topological properties as neighbourhood relationships Cleft-right polygon" identification), directionality Cfrom node-to node" identification) and connectivity Clinks sharing flows"). It allows complex spatial queries (so called views) and operations, such as generalizations of land uses, aggregations of zones or polygon overlays. Because of these capabilities, GIS is very flexible to answering "what-is" questions.
The data demands of MDS are mainly defined by the needs of the transportation model. In order to prepare the aforementioned TM input, both network (e.g. minor roads. stops, intersections) and zonal demand features (e.g. land uses, workplaces) have been consolidated or generalized at the GIS level. Interface routines convert related tables of the unified network into link. node. turn. and line tables of the TM. The reverse data transfer of the TM output back to the GIS level refers to the superior GIS capability to visually display future scenarios and perform follow-up spatial operations.
Transportation models are applied to predict changes in road use and ridership of transport systems in response to changes in regional development and networks. Transport models require on the supply side, abstract, but highly connected networks of straight lines and nodes, link and node costs, transit fare matrices, terminal cost vectors (e.g. walking time to means of transport), and other related attributes and functions . The Public Transport (PT) network contain routes possessing the topological property and consisting of a sequence of nodes (stops). On the demand side models require O-D matrices of (person or vehicle) trip interchanges. Transport models are well equipped with complex network or matrix calculators and efficient algorithms to equilibrate supply and demand when examining "what-if' scenarios. Their output consists of (a) estimated traffic volumes and congested travel times by link, (b) matrices of generalized costs by zone pair (cost tables).
3. MDS GEOGRAPHICAL DATA BASE The conceptual design of the MDS geographical data base rests upon an entityrelationship (E-R) model. containing defined entity types (spatial types such as points, links, polygons or aspatial such as household members). features. i.e. combinations of several entities (such as routes. aggregate zones or households) and connections between them. Entities may be defined by their identifier. (non)metric geographic properties and descriptive attributes. There exist different types of relationships between distinct entity types. In other words. the E-R model pertains both to geographical and attribute infornlation and presents tables needed to organize processed data as well as relationships bet\\een them. Its spatial properties are maporiented and its attributes record-oriented.
Transportation models process spatial (zonal) variables and their networks have a geographic extent. Network intersections normally preserve the geometric (positional) and topological properties of GIS nodes. Line simplifications (straight instead of curved links) of the TM usually preserve topological properties, such as the network connectivity. Compiling both systems in the transportation context leads to a more efficient data base management and planning process.
The three central features of the MDS data base are transportation networks. polygon aggregates containing land use and socioeconomic information. and trip tables expressing trm'el demand relationships between spatial entities (e.g. zones).
418
The MDS digital base maps were typically generated from medium-scale 1:5000 conventional maps (prepared in 1988, partly updated in 1996) and exhibit a spatial precision of about +/- 5 meters. This spatial data quality is sufficient for transportation planning applications. The attribute information comprises typically georeferenced data, collected within the frame of the quite extensive 1996 MDS surveys.
3.2 Public tran.'port network
The public transport network is structured as a route system. built up on top of the GIS arcs of the digital base map using the dynamic segmentation technique (see section 5). It contains additional routes with exclusive rights-of-way. The four transit modal networks (mixed-traffic urban OASA buses and interurban KTEL buses. ISAP metro and OSE rail) refer to the following entity types and related tables. Directional routes have a many-to-many relationship to PT vehicles. stops a one-to-many relationship to routes. Important operational attributes are related to the sections and the routes (e.g. headways). Prospective point-in-polygon operations will relate fare zones to stops (one-to-many relationship ).
Referring to the physical design of the MDS geographical data base, spatial information is stored in an ARCIlNFO server and attribute information in an ORACLE server. both communicating together via a standard TCPIIP protocol.
3.1 Road network 3.3
Core component of the data base is a digital map of the detailed road network of the study area, providing the actual geometry of the road axes, the intersection co-ordinates, the street addresses and street numbering information along the axes. The map has also been used as part of a vehicle navigation system, so it's level of spatial accuracy is rather satisfactory. A limited graphic editing work had still to be performed. A network abstraction, for use by the transportation model, has been extracted from this base map. The extraction is a highly connected link-node map of the so called "Main Road Network" of the study area with an 1.7 ratio of 7.120 links to 4.208 generalized nodes, where by the digital base map reveals a 1.47 ratio of 172.000 arcs to 117.000 nodes. GIS and TM node co-ordinates of course coincide, where by complex topologies (e.g. complex intersections) have been collapsed already at the GIS level to a single node. Pseudo nodes have been removed. Inventory, functional and operational attributes related to the Main Road Network have been also coupled to the relevant map.
(ini}ied network
A unified network has been setup at the GIS leveL resulting in a further increase of connectivity. The Main Road Network has been unified with the bus-used part of the secondary road network. the transit alignment with exclusive rights-of-way. the "bus-only" lanes, and the walking links including pedestrian transfer links. Zonal connectors to the network have also been defined. The incorporation process involved imputation of new nodes. link splitting. and adjustment of the attribute tables. The generalization of bus stops to superstops by merging the former with the nearest node at the GIS level. has been followed by an attribute generalization of the related tables. for instance replication of functional attributes or proportional split of metric attributes. More laborious is the generalization of (tr,ll1sfer) boardings and alightings. Quality controls and coding corrections required. however. several weeks. The procedure has resulted into a one-to-one relationship between Cl GIS section and a TM segment or between a stop-to-stop segment and a link at the TM levcL
The main entity types and related tables of the road map refer to GIS arcs and nodes. intersections in one-to-many relationship to approaches in one-to-many relationship to turning movements, and links having a oneto-many subset relationship to link sections. Several operational attributes are related to the links.
Future new PT and road infrastructure projects built up parts of alternative network scenarios. Digitized vcctor maps of the purposed projects have been merged with the master base map. lIsmg rubber-sheeting techniques in order to match the coverages. Some missing arcs have also been drawn with on-screen digitizing .
419
3.4 Land use map
needed in order to ensure that the right address has been geocoded. because Cl one-tomany relationship exists between street names and municipalities.
The second essential component of the 'MDS geographical data base is a digital map of ··land use polygons". It has been compiled after an ex1ensive survey of detailed land uses in the study area. Area! entity types based on that map follow a generalization hierarchy, whereby an upper level entity is a generalization of a lower level entity. Accordingly, one-to-many relationships link successively 117 municipalities to 450 zone groups, 1233 traffic analysis zones, 70.000 building blocks and, finally, 200.000 land use polygons. In each hierarchical spatial level (excL zone level), the area! entities consist of non-overlapping polygons. Circulation surface equates with the difference between zones and contained building blocks. The aggregate areal entities are, in a broad sense. consistent with the detailed road network map.
Respondents do not always know full addresses and may give road intersections. landmarks, or even incomplete infomlation instead. A reference file including 3000 landmarks (e.g. schools. squares. churches) has also been used . Special GIS routines have been used to match the recorded addresses to the street reference or landmark directol!: entries. MDS interview surveys produced about 55% full addresses and 45'1., exact addresses (in fom1 of intersections or landmarks not included in the landmark reference file) or incomplete addresses. due to limited reported infomlation or non-existing field information (no street numbers or street names existing) .
The GIS-based transportation planning approach of the 'MDS is one of few cases, where planning resources used, include not only a basic centerline map, but also a detailed land use polygon map.
About 85% (= matching rate) of the full addresses has been successfully located. A polygon overlay of the zone map on the map of the matched addresses assigned the point entities to zones (point-in-polygon operation) . The 15% rejection rate of unmatched addresses is ascribed either to severe street name misspellings (partly due to the complex greek grammar) or missing information or problems with the base map. The application provided listings of rejected matches together with codes describing certain rejection reasons. About (jO% of the rejected addresses possessing a one-to-one relationship to zones have been manually coded. i.e. assigned to the latter. Another 40% of the rt:iected addresses lying on zone boundaries have been assigned to a zone. according to the allocation ratios of already assigned addresses.
4. GEOCODING APPLICATION The 'MDS transportation model needs O-D trip tables expressing travel demand relationships between zones. To set up trip tables, the trip end addresses recorded during interview surveys must be located and coded. i.e. assigned to zones. A building block-level positional accuracy is desired (having in mind future re-zoning) and at least a zone-level accuracy (envisaging actual zone coding). As a result of the extensive O-D interview surveys conducted within the frame of the MDS. over 850.000 trip end addresses of differing precision levels have been reported . Because of the large number of addresses. an automated GIS geocoding process was envisaged from the very beginning.
From the '+5% exact or incomplete addresses. about (jO'1., have been manually assigned to a zone (one-to-one relationship). and 40% allocated according to the ratios of the previously assigned addresses (one-to-many relationship bet\yeen addresses and zones). Incompletely recorded information refers primarily to municipality-only-addresses. then {municipality + street name: -only-addresses. and then (street name + street number}-onlyaddresses.
In order to carry out the abovementioned task, a street reference file has been used. attributing full addresses, i.e. street name. municipality, range of left/right street numbers by road segment (within urban areas), to the digital base map of the detailed road network. The street name records of the file have been standardized in a preprocessing step. Municipality codes were
OveralL the success rate of the automated geocoding depends on the quality of the recorded address information . the extent of
420
non-existing field information as well as the information contained in the reference files. Because of the large number of reported addresses, the automated geocoding has led, when all has been considered, to significant time and effort savings. An enrichment of the georeferencing files with an intersection directory is, however, recommended.
choropleth maps may illustrate existing or future point (e.g. traffic accidents at intersections). linear (e.g traffic noise bandwidths) or areal patterns (e.g. shadings of zonal attributes). An illustration of the 1990 car ownership rate at the municipality level is provided (Fig. 2). The corresponding information of the MDS geographical data base is stored in 5 class intervals. The classes are displayed using different colour shadings.
5. TRANSIT ROUTE SYSTEM APPLICATION
The spatial operations capability of a transportation-oriented GIS has been used in numerous MDS applications. A basic application refers to the service area analysis of the PT route system. The size of the service area of a PT route indicates the route 's accessibility to the public. Contours of equal distance (so called buffers) around stops of a route define its service area .
A directed PT route is an ordered sequence of stops defining stop-to-stop segments or sections. It is more natural to linearly reference route stops according to their odometric distance from the route starting point (e.g bus terminal), rather than providing their xly-coordinates. Besides that, several routes may operate (overlap) on the same stopto-stop segment. PT sections applying linear measures of location and possessing one-tomany relationship to routes are more efficiently modelled when using the so called dynamic segmentation data model, than the static arc-node data model. Route systems are essentially virtual networks that overlay the underlying digital base network without altering the topological structure of the latter, e.g. splitting a link to impute a stop. The advantage is a smaller redundancy of stored information and, consequently, a faster access to information.
An acceptable walking distance of 400 m around bus stops and 5()0 m around rail stations as upper thresholds has been assumed. The threshold approximates the product of a straight-line distance times a mean deviation factor for using sidcwalks along the boundaries of small- to medium-sized blocks. typical for the Greater Athens Area. T"vo consecutive GIS operations are involved in the above mentioned application. First. an automated buffering around stops defines the buffer areas. Second. buffer areas are overlaid (superimposed) onto zone polygons and the concerned population (residents. workplaces etc.) is estimated on the basis of the amount of overlap. i.e. the area ratio (map algebra). The procedure is based on the assumption of an evenly distributed population within each zone.
As already mentioned in subsection 3.2. the MDS uses the dynamic segmentation technique to map the PT route system at the GIS level. Current limitations of the technique (split routes, bifurcations etc.) providing barriers to data processing had to be rectified. The unified PT route system of the study area contains 476 directional routes belonging to 276 PT lines (some of them are circular) . Route maps. as products of a corresponding GlS application, give a geographic display of directed route paths, stop locations, and names of streets that the route passes. They have proved to be an important support and orientation tool for the MDS PT planners and modellers (Fig. I).
The map overlay illustrated (Fig. :1) refers to the working (day) population . The service area of the station (ISAP-Omonia ) indicates in this case of a typical attraction district. the zonal potential for initial boarding passengers (evening peak) and final alighting passengers (morning peak) respectively. The O\ulap of buffers around stops indicates the PT system coverage o\·er the study area . It helps to select alternative routing plans and to optimize stop spacing in order to reduce redundancy in coverage as \yell as transit time for through riders .
6. MAPPING APPLICATIONS AND OVERLAYS GIS possess better visual display capabilities than transportation models. Thematic or
421
Table 1 Parameter estimates Band t-values
7. SPATIAL DATA ANALYSIS Traditionally, GIS development was driven by the needs of data management rather than those of data analysis. However, spatial data handling in the land use and transportation context is of increasing importance. Statistical analysis of spatial data may help to identify relationships between spatially aggregated (trip-inducing) socioeconomic and land use intensity variables. Getting insights about causal relationships in a spatial context enables predictions of travel-generating spatial phenomena.
~
Consbmt POP PPHA GFARATIO Sample
R'
PPHA
RES GFA Modd
0. 29 -0.00 1
423 9 -0.19 -2 22.5 1.2 73 0.39
~
73 0.96
73 0.71
.~
The percentage of high income households has much stronger contribution into e:>.:plaining the variation of the share of carowning households_ than the gross residential density (Model 2). The latter is, as e:>.:pected, negatively correlated \-"ith the car ownership. The impact of the share of high income households is almost 20 times stronger and more stable than that of the gross floor area ratio on the residential gross floor area per inhabitant (Model 3). The influence of the gross residential density is again negative. as expected, but small. The GIS spatial technology may be also used to examine the neighbourhood effect of surrounding municipalities on each dependent variable. Such an examination uses a matrix of contiguity of municipalities having common boundaries_ by exploiting the topological query capabilities of the GIS. The spatial correlation among municipalities is not proved, however. to be significant.
Referring to the 73 municipalities of the Athens Metropolitan Area, their 1996 mean values are:
GFARATIO
CAROWN% Modd 2 [J 30 o. n
'<'
0.1 '
JUNe_ol.
The GIS spatial analysis capabilities have been used to process information at the level of municipality contained in the MDS geographical data base, and to derive the distribution of mobility-related socioeconomic and land use intensity characteristics. The characteristics refer to (a) non-basic employment NONBASIC, specified as a function of the population POP, (b) percentage of car owning households CAROWN_%, specified as a function of the share of high income (>400,000 Drchs/month) households HIN C_% and the gross residential density (persons per ha) PPHA, (c) residential gross floor area per inhabitant (residential square meters per inhabitant, incl. public and auxiliary residential area, vacant residences etc.). RES_GFA, as a function of HINC_%, PPHA and effective (all buildings) gross floor area ratio GFARATIO. The spatial data analysis is based on the regression model.
C.A.ROWN % RES_ GFA HINC_ %
NOI'<""BASIC Model I
Attribut<:
8. CONCLUDING REMARKS OveralL the start-up costs of the GIS-based transportation planning. especially for data collection, are high. but once the initial stage is over. the benefits of follow-up studies are much higher.
62% 57 sq.m. 21% 143 0,8
However. a periodic maintenance of the geographical data base with actual information is strongly recommended. otherwise it will quickly become useless. As a general rule. attribute updating is more important in central urban areas. whereby in fringe areas both geometric (e.g. new street alignments or extension of the built-up areas) and attribute updating are needed.
The 1996 population amounts to 3,810,338 inhabitants and the non-basic employment sector 362,000 employed. The non basic employment (Model I) has a very strong and stable relationship to the population. The GARIN-LOWRY theory to forecast future distributions of population and employment is in that sense corroborated in the Athens context.
422
The establishment of a georeferenced census data collection by the National Statistical Service, following the current practice in Western European countries, would greatly facilitate the abovementioned maintenance task.
ACKNOWLEDGEMENTS The study was fully funded by Attiko Metro.
423
,',' /
:,;'/
/ ·r/.
/. ',,",,:, '/,.
MDS ROUTE
MAP
Main Road Network Bus Route Trolley Bus Route
Fig.1 Illustration of a PT route system
424
llEIKTHI IlllOKTHIIAI 1,)(. AYTOKINHTaN ANA 1000 KATOIKOYI llHWlN N. CAR OWNERSHIP RATE OF ATTICA MUN1CI"'-UTIES - 1996
AUToodvrlTO I.X. """ 1000 ~QToIotou~ PII_ car per 1000 inhabitants
<
215
~
!5l!
•• •
2111 - 233 234 -291 291 - 343
> 344
Fig.2 Municipal car ownership rate
425
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ciJ.IStationTAZ
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07! 070 073 073 073 073 07S 07S 071 073 071 073 073 073 071 073 073 073 073 07S 073 073
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8078 8077 8071 1237 8231 8241 8231 .242 .0.0 .240 01.1 .0.1 .,10 .200
.,It
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Partition
2. O' 0.14 2 . 01
" ' 3 0 . 770 '133 . 7" ,.,.1.1.0 '170 . • 1. '002'.110 '1113 . • 10 441" . 110 10'0' . 710 47".177 41114 . 0'0 '1237 . 110 .1.4. 101 710'4 . 710 '01" . 740 4313.000 411'1 . 010 • • , I • . 100 '0301 . 130 13704 . '70 31. 7S4 21114 . • 10 " 1 1 ' . '10 11711 . 7'0 7241 . 422
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-
Fig .3
zone (T AZ) boundary
street centerline
GIS-BASED TRANSPORTATION PLANNING AND ANALYSIS: A PRACTICAL IMPLEMENTATION Alexandros Deloukas lIias Kokkinos Georgios Kiousis Despina Zannou
Attiko Metro A.E.
Abstract: The potential of using advanced geographic information system (GIS) technology in transportation is illustrated in the context of the Metro Development Study (MDS). Data management issues related to the conceptual design of the MDS geographical data base and the representation of public transport routes are discussed. A geocoding application serving the needs of the transportation model is described. Examples of data display (thematic maps) and data manipulation (map overlays) are given, and findings of spatial data analysis are presented. The overview of GIS-based transportation applications within MDS illustrates the advantages of integrating both systems. Keywords: GIS and transportation modelling, Conceptual design. E-R model. Digital map, Geocoding, Route system, Spatial data analysis.
1.
INTRODUCTION
The aim of this paper is, first to stress the complementarity of geographic information systems (GIS) and transportation models (TM) in general, and their specific integration within the frame of the Metro Development Study (MDS) , a comprehensive multi-modal transportation planning effort being undertaken by Attiko Metro. Second. to illustrate the structure of the large and complex MDS geographical data base. Third, to demonstrate transportation-related applications of GIS, currently underway within the MDS context. The structure of the paper is as follows :
network as well as of land uses are based. is in that context described. The following sections are organized along applications referring to basic spatial types of entities such as points. lines and areas with boundaries. In section 4. the results of geocoding of point entities. such as trip end addresses recorded during the MDS surveys are presented. The fifth section explains the use of the dynamic segmentation technique 'vvithin MDS to represent linear features, such as public transport toutes. In section 6. the results of several map transformations of MDS spatial data are displayed. The seyenth section discusses empirical findings of multivariate spatial statistics interfaced with transformations of the MDS geographical data base.
In section 2, the specific features of both systems and the way they are coupled together within the frame of MDS are described. The third section contains the conceptual design of the MDS geographical data base. The entity-relationship model on which the digital maps of road and public transport
The paper ends with some suggestions about a regular updating of the geographical data base.
417
2. GIS AND TRANSPORT MODEL INTEGRATION
Within the frame of MDS. a pragmatic approach of loosely coupling both systems (via so called "cold linkages") has been pursued. Automated linkages between GIS and TM level are provided via interface routines, which convert data tables into a format that each system can read. Data exchange has been greatly enabled by the common coding of network features at bOtll levels. The specific platforms used in the study are (a) ARCINFO (workstation version 7.01), a vector-based GIS package, (b) EtvfME/2 (release 8), a multimodal equilibrium assignment transport model package.
As a spatial information system, GIS is an appropriate platform integrating the geometry and topology of spatial objects with attribute data expressing metric or non-metric properties linked to the spatial objects. GIS can represent realistically the geometry of transportation networks, such as shape, distance and positional properties. It can also express topological properties as neighbourhood relationships Cleft-right polygon" identification), directionality Cfrom node-to node" identification) and connectivity Clinks sharing flows"). It allows complex spatial queries (so called views) and operations, such as generalizations of land uses, aggregations of zones or polygon overlays. Because of these capabilities, GIS is very flexible to answering "what-is" questions.
The data demands of MDS are mainly defined by the needs of the transportation model. In order to prepare the aforementioned TM input, both network (e.g. minor roads. stops, intersections) and zonal demand features (e.g. land uses, workplaces) have been consolidated or generalized at the GIS level. Interface routines convert related tables of the unified network into link. node. turn. and line tables of the TM. The reverse data transfer of the TM output back to the GIS level refers to the superior GIS capability to visually display future scenarios and perform follow-up spatial operations.
Transportation models are applied to predict changes in road use and ridership of transport systems in response to changes in regional development and networks. Transport models require on the supply side, abstract, but highly connected networks of straight lines and nodes, link and node costs, transit fare matrices, terminal cost vectors (e.g. walking time to means of transport), and other related attributes and functions . The Public Transport (PT) network contain routes possessing the topological property and consisting of a sequence of nodes (stops). On the demand side models require O-D matrices of (person or vehicle) trip interchanges. Transport models are well equipped with complex network or matrix calculators and efficient algorithms to equilibrate supply and demand when examining "what-if' scenarios. Their output consists of (a) estimated traffic volumes and congested travel times by link, (b) matrices of generalized costs by zone pair (cost tables).
3. MDS GEOGRAPHICAL DATA BASE The conceptual design of the MDS geographical data base rests upon an entityrelationship (E-R) model. containing defined entity types (spatial types such as points, links, polygons or aspatial such as household members). features. i.e. combinations of several entities (such as routes. aggregate zones or households) and connections between them. Entities may be defined by their identifier. (non)metric geographic properties and descriptive attributes. There exist different types of relationships between distinct entity types. In other words. the E-R model pertains both to geographical and attribute infornlation and presents tables needed to organize processed data as well as relationships bet\\een them. Its spatial properties are maporiented and its attributes record-oriented.
Transportation models process spatial (zonal) variables and their networks have a geographic extent. Network intersections normally preserve the geometric (positional) and topological properties of GIS nodes. Line simplifications (straight instead of curved links) of the TM usually preserve topological properties, such as the network connectivity. Compiling both systems in the transportation context leads to a more efficient data base management and planning process.
The three central features of the MDS data base are transportation networks. polygon aggregates containing land use and socioeconomic information. and trip tables expressing trm'el demand relationships between spatial entities (e.g. zones).
418
The MDS digital base maps were typically generated from medium-scale 1:5000 conventional maps (prepared in 1988, partly updated in 1996) and exhibit a spatial precision of about +/- 5 meters. This spatial data quality is sufficient for transportation planning applications. The attribute information comprises typically georeferenced data, collected within the frame of the quite extensive 1996 MDS surveys.
3.2 Public tran.'port network
The public transport network is structured as a route system. built up on top of the GIS arcs of the digital base map using the dynamic segmentation technique (see section 5). It contains additional routes with exclusive rights-of-way. The four transit modal networks (mixed-traffic urban OASA buses and interurban KTEL buses. ISAP metro and OSE rail) refer to the following entity types and related tables. Directional routes have a many-to-many relationship to PT vehicles. stops a one-to-many relationship to routes. Important operational attributes are related to the sections and the routes (e.g. headways). Prospective point-in-polygon operations will relate fare zones to stops (one-to-many relationship ).
Referring to the physical design of the MDS geographical data base, spatial information is stored in an ARCIlNFO server and attribute information in an ORACLE server. both communicating together via a standard TCPIIP protocol.
3.1 Road network 3.3
Core component of the data base is a digital map of the detailed road network of the study area, providing the actual geometry of the road axes, the intersection co-ordinates, the street addresses and street numbering information along the axes. The map has also been used as part of a vehicle navigation system, so it's level of spatial accuracy is rather satisfactory. A limited graphic editing work had still to be performed. A network abstraction, for use by the transportation model, has been extracted from this base map. The extraction is a highly connected link-node map of the so called "Main Road Network" of the study area with an 1.7 ratio of 7.120 links to 4.208 generalized nodes, where by the digital base map reveals a 1.47 ratio of 172.000 arcs to 117.000 nodes. GIS and TM node co-ordinates of course coincide, where by complex topologies (e.g. complex intersections) have been collapsed already at the GIS level to a single node. Pseudo nodes have been removed. Inventory, functional and operational attributes related to the Main Road Network have been also coupled to the relevant map.
(ini}ied network
A unified network has been setup at the GIS leveL resulting in a further increase of connectivity. The Main Road Network has been unified with the bus-used part of the secondary road network. the transit alignment with exclusive rights-of-way. the "bus-only" lanes, and the walking links including pedestrian transfer links. Zonal connectors to the network have also been defined. The incorporation process involved imputation of new nodes. link splitting. and adjustment of the attribute tables. The generalization of bus stops to superstops by merging the former with the nearest node at the GIS level. has been followed by an attribute generalization of the related tables. for instance replication of functional attributes or proportional split of metric attributes. More laborious is the generalization of (tr,ll1sfer) boardings and alightings. Quality controls and coding corrections required. however. several weeks. The procedure has resulted into a one-to-one relationship between Cl GIS section and a TM segment or between a stop-to-stop segment and a link at the TM levcL
The main entity types and related tables of the road map refer to GIS arcs and nodes. intersections in one-to-many relationship to approaches in one-to-many relationship to turning movements, and links having a oneto-many subset relationship to link sections. Several operational attributes are related to the links.
Future new PT and road infrastructure projects built up parts of alternative network scenarios. Digitized vcctor maps of the purposed projects have been merged with the master base map. lIsmg rubber-sheeting techniques in order to match the coverages. Some missing arcs have also been drawn with on-screen digitizing .
419
3.4 Land use map
needed in order to ensure that the right address has been geocoded. because Cl one-tomany relationship exists between street names and municipalities.
The second essential component of the 'MDS geographical data base is a digital map of ··land use polygons". It has been compiled after an ex1ensive survey of detailed land uses in the study area. Area! entity types based on that map follow a generalization hierarchy, whereby an upper level entity is a generalization of a lower level entity. Accordingly, one-to-many relationships link successively 117 municipalities to 450 zone groups, 1233 traffic analysis zones, 70.000 building blocks and, finally, 200.000 land use polygons. In each hierarchical spatial level (excL zone level), the area! entities consist of non-overlapping polygons. Circulation surface equates with the difference between zones and contained building blocks. The aggregate areal entities are, in a broad sense. consistent with the detailed road network map.
Respondents do not always know full addresses and may give road intersections. landmarks, or even incomplete infomlation instead. A reference file including 3000 landmarks (e.g. schools. squares. churches) has also been used . Special GIS routines have been used to match the recorded addresses to the street reference or landmark directol!: entries. MDS interview surveys produced about 55% full addresses and 45'1., exact addresses (in fom1 of intersections or landmarks not included in the landmark reference file) or incomplete addresses. due to limited reported infomlation or non-existing field information (no street numbers or street names existing) .
The GIS-based transportation planning approach of the 'MDS is one of few cases, where planning resources used, include not only a basic centerline map, but also a detailed land use polygon map.
About 85% (= matching rate) of the full addresses has been successfully located. A polygon overlay of the zone map on the map of the matched addresses assigned the point entities to zones (point-in-polygon operation) . The 15% rejection rate of unmatched addresses is ascribed either to severe street name misspellings (partly due to the complex greek grammar) or missing information or problems with the base map. The application provided listings of rejected matches together with codes describing certain rejection reasons. About (jO% of the rejected addresses possessing a one-to-one relationship to zones have been manually coded. i.e. assigned to the latter. Another 40% of the rt:iected addresses lying on zone boundaries have been assigned to a zone. according to the allocation ratios of already assigned addresses.
4. GEOCODING APPLICATION The 'MDS transportation model needs O-D trip tables expressing travel demand relationships between zones. To set up trip tables, the trip end addresses recorded during interview surveys must be located and coded. i.e. assigned to zones. A building block-level positional accuracy is desired (having in mind future re-zoning) and at least a zone-level accuracy (envisaging actual zone coding). As a result of the extensive O-D interview surveys conducted within the frame of the MDS. over 850.000 trip end addresses of differing precision levels have been reported . Because of the large number of addresses. an automated GIS geocoding process was envisaged from the very beginning.
From the '+5% exact or incomplete addresses. about (jO'1., have been manually assigned to a zone (one-to-one relationship). and 40% allocated according to the ratios of the previously assigned addresses (one-to-many relationship bet\yeen addresses and zones). Incompletely recorded information refers primarily to municipality-only-addresses. then {municipality + street name: -only-addresses. and then (street name + street number}-onlyaddresses.
In order to carry out the abovementioned task, a street reference file has been used. attributing full addresses, i.e. street name. municipality, range of left/right street numbers by road segment (within urban areas), to the digital base map of the detailed road network. The street name records of the file have been standardized in a preprocessing step. Municipality codes were
OveralL the success rate of the automated geocoding depends on the quality of the recorded address information . the extent of
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non-existing field information as well as the information contained in the reference files. Because of the large number of reported addresses, the automated geocoding has led, when all has been considered, to significant time and effort savings. An enrichment of the georeferencing files with an intersection directory is, however, recommended.
choropleth maps may illustrate existing or future point (e.g. traffic accidents at intersections). linear (e.g traffic noise bandwidths) or areal patterns (e.g. shadings of zonal attributes). An illustration of the 1990 car ownership rate at the municipality level is provided (Fig. 2). The corresponding information of the MDS geographical data base is stored in 5 class intervals. The classes are displayed using different colour shadings.
5. TRANSIT ROUTE SYSTEM APPLICATION
The spatial operations capability of a transportation-oriented GIS has been used in numerous MDS applications. A basic application refers to the service area analysis of the PT route system. The size of the service area of a PT route indicates the route 's accessibility to the public. Contours of equal distance (so called buffers) around stops of a route define its service area .
A directed PT route is an ordered sequence of stops defining stop-to-stop segments or sections. It is more natural to linearly reference route stops according to their odometric distance from the route starting point (e.g bus terminal), rather than providing their xly-coordinates. Besides that, several routes may operate (overlap) on the same stopto-stop segment. PT sections applying linear measures of location and possessing one-tomany relationship to routes are more efficiently modelled when using the so called dynamic segmentation data model, than the static arc-node data model. Route systems are essentially virtual networks that overlay the underlying digital base network without altering the topological structure of the latter, e.g. splitting a link to impute a stop. The advantage is a smaller redundancy of stored information and, consequently, a faster access to information.
An acceptable walking distance of 400 m around bus stops and 5()0 m around rail stations as upper thresholds has been assumed. The threshold approximates the product of a straight-line distance times a mean deviation factor for using sidcwalks along the boundaries of small- to medium-sized blocks. typical for the Greater Athens Area. T"vo consecutive GIS operations are involved in the above mentioned application. First. an automated buffering around stops defines the buffer areas. Second. buffer areas are overlaid (superimposed) onto zone polygons and the concerned population (residents. workplaces etc.) is estimated on the basis of the amount of overlap. i.e. the area ratio (map algebra). The procedure is based on the assumption of an evenly distributed population within each zone.
As already mentioned in subsection 3.2. the MDS uses the dynamic segmentation technique to map the PT route system at the GIS level. Current limitations of the technique (split routes, bifurcations etc.) providing barriers to data processing had to be rectified. The unified PT route system of the study area contains 476 directional routes belonging to 276 PT lines (some of them are circular) . Route maps. as products of a corresponding GlS application, give a geographic display of directed route paths, stop locations, and names of streets that the route passes. They have proved to be an important support and orientation tool for the MDS PT planners and modellers (Fig. I).
The map overlay illustrated (Fig. :1) refers to the working (day) population . The service area of the station (ISAP-Omonia ) indicates in this case of a typical attraction district. the zonal potential for initial boarding passengers (evening peak) and final alighting passengers (morning peak) respectively. The O\ulap of buffers around stops indicates the PT system coverage o\·er the study area . It helps to select alternative routing plans and to optimize stop spacing in order to reduce redundancy in coverage as \yell as transit time for through riders .
6. MAPPING APPLICATIONS AND OVERLAYS GIS possess better visual display capabilities than transportation models. Thematic or
421
Table 1 Parameter estimates Band t-values
7. SPATIAL DATA ANALYSIS Traditionally, GIS development was driven by the needs of data management rather than those of data analysis. However, spatial data handling in the land use and transportation context is of increasing importance. Statistical analysis of spatial data may help to identify relationships between spatially aggregated (trip-inducing) socioeconomic and land use intensity variables. Getting insights about causal relationships in a spatial context enables predictions of travel-generating spatial phenomena.
~
Consbmt POP PPHA GFARATIO Sample
R'
PPHA
RES GFA Modd
0. 29 -0.00 1
423 9 -0.19 -2 22.5 1.2 73 0.39
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73 0.96
73 0.71
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The percentage of high income households has much stronger contribution into e:>.:plaining the variation of the share of carowning households_ than the gross residential density (Model 2). The latter is, as e:>.:pected, negatively correlated \-"ith the car ownership. The impact of the share of high income households is almost 20 times stronger and more stable than that of the gross floor area ratio on the residential gross floor area per inhabitant (Model 3). The influence of the gross residential density is again negative. as expected, but small. The GIS spatial technology may be also used to examine the neighbourhood effect of surrounding municipalities on each dependent variable. Such an examination uses a matrix of contiguity of municipalities having common boundaries_ by exploiting the topological query capabilities of the GIS. The spatial correlation among municipalities is not proved, however. to be significant.
Referring to the 73 municipalities of the Athens Metropolitan Area, their 1996 mean values are:
GFARATIO
CAROWN% Modd 2 [J 30 o. n
'<'
0.1 '
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The GIS spatial analysis capabilities have been used to process information at the level of municipality contained in the MDS geographical data base, and to derive the distribution of mobility-related socioeconomic and land use intensity characteristics. The characteristics refer to (a) non-basic employment NONBASIC, specified as a function of the population POP, (b) percentage of car owning households CAROWN_%, specified as a function of the share of high income (>400,000 Drchs/month) households HIN C_% and the gross residential density (persons per ha) PPHA, (c) residential gross floor area per inhabitant (residential square meters per inhabitant, incl. public and auxiliary residential area, vacant residences etc.). RES_GFA, as a function of HINC_%, PPHA and effective (all buildings) gross floor area ratio GFARATIO. The spatial data analysis is based on the regression model.
C.A.ROWN % RES_ GFA HINC_ %
NOI'<""BASIC Model I
Attribut<:
8. CONCLUDING REMARKS OveralL the start-up costs of the GIS-based transportation planning. especially for data collection, are high. but once the initial stage is over. the benefits of follow-up studies are much higher.
62% 57 sq.m. 21% 143 0,8
However. a periodic maintenance of the geographical data base with actual information is strongly recommended. otherwise it will quickly become useless. As a general rule. attribute updating is more important in central urban areas. whereby in fringe areas both geometric (e.g. new street alignments or extension of the built-up areas) and attribute updating are needed.
The 1996 population amounts to 3,810,338 inhabitants and the non-basic employment sector 362,000 employed. The non basic employment (Model I) has a very strong and stable relationship to the population. The GARIN-LOWRY theory to forecast future distributions of population and employment is in that sense corroborated in the Athens context.
422
The establishment of a georeferenced census data collection by the National Statistical Service, following the current practice in Western European countries, would greatly facilitate the abovementioned maintenance task.
ACKNOWLEDGEMENTS The study was fully funded by Attiko Metro.
423
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Main Road Network Bus Route Trolley Bus Route
Fig.1 Illustration of a PT route system
424
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Fig.2 Municipal car ownership rate
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Fig .3
zone (T AZ) boundary
street centerline