Toward the design of intelligent traveler information systems

TRANSPORTATION RESEARCH PART C

Transportation Research Part C 6 (1998) 157±172

Toward the design of intelligent traveler information systems Je€rey L. Adler a,*, Victor J. Blue b,1 a

Department of Civil Engineering, Center for Infrastructure and Transportation Studies, Rensselaer Polytechnic Institute, Troy, NY 12180-3590, USA b New York State Department of Transportation, Poughkeepsie, NY 12603, USA Received 12 February 1997; received in revised form 25 June 1998

Abstract The emergence of Intelligent Transportation Systems (ITS) has fostered the development of advanced traveler information systems (ATIS). These systems are designed to assist travelers in making pre-trip and enroute travel choice decisions. It is contended that while many traveler information systems are innovative and make use of cutting edge technologies, they lack real machine intelligence and therefore may be limited in their ability to service the traveling public over the long-run. The purpose of this paper is to present a vision of the next generation traveler information system, termed Intelligent Traveler Information Systems (ITIS) in which arti®cial intelligence techniques are drawn upon to create systems capable of providing travelers with more personalized planning assistance. # 1998 Elsevier Science Ltd. All rights reserved. Keywords: Traveler information systems; ATIS; Route guidance; Arti®cial intelligence; ITS

1. Introduction Advanced Traveler Information Systems (ATIS) are an integral component of Intelligent Transportation Systems (ITS). The provision of real-time information to travelers will lead to more ecient distribution of travelers to routes and modes. For the individual, using ATIS can lead to more ecient travel choices and help reduce anxiety and stress associated with travel planning, way®nding, and navigating through the network. For the system as a whole, if enough travelers use ATIS there will be signi®cant reductions in travel time, delay, fuel consumption, and emissions. * Corresponding author. Tel.: +1-518-276-6938; Fax: +1-518-276-4833; e-mail: [email protected] 1 Tel.: +1-914-431-7090; Fax: +1-914-431-7923; e-mail: [email protected] 0968-090X/98/$Ðsee front matter # 1998 Elsevier Science Ltd. All rights reserved. PII: S 0968-090X (9 8 ) 0 0 0 1 2 - 6

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Research on driver information technologies dates back to the 1950s and has evolved over the past 40+ years as part of greater e€orts in creating urban trac surveillance and control systems. Although technologies have advanced, the primary goals for providing travelers with information are still the same, namely, better management of trac ¯ow, enhanced driving operations, and improving traveler safety. In the 1960s and early 1970s large metropolitan communities, such as Los Angeles, Detroit, and Chicago, began to research, develop, and test advanced technologies for trac surveillance and real-time information dissemination. Work on traveler information systems focused on using visual displays to provide drivers with trac condition and diversion information (Weinberg et al., 1966; Highway Research Board, 1971, 1973). It was believed that with the advancement of more powerful computers and other technologies, a centralized coordination of highway systems through real-time trac surveillance and control strategies would be useful to improve operations. By the 1970s several trac surveillance and control projects were in full implementation around the country and the world (West, 1969; Whitten, 1969; Carlson and Benke, 1973; Nenzi and Anglisant, 1974). Interest in human factors issues increased as it became important to help improve the design of visual displays used to provide both real-time trac information as well as diversion information at key intersections and ramps within large urban areas. (Dudek, 1970; Dudek and Jones, 1971; Pretty et al., 1971). In the 1970s e€orts to develop in-vehicle route guidance systems were initiated. The objective was to improve information dissemination and driver compliance by bringing the information right to the driver. The ®rst major project, sponsored by the Federal Highway Administration in 1970, was ERGS (Electronic Route Guidance System). The research was aimed at providing drivers with in-vehicle directional guidance based on the desired origin-destination trip plan (Rosen et al., 1970). In 1971, the government quickly abandoned this project. In the mid 1970s, Japan began a series of research projects based on the ERGS model. It was not until the 1980s, when advances in computer and communication technologies reached such a threshold, that e€orts to build and implement traveler information systems gathered momentum. By the end of the 1980s centrally controlled, sophisticated variable message signs and highway advisory systems were becoming more common across many metropolitan areas. In addition, there were several invehicle route guidance systems (IVRGS) being tested across the globe, particularly in Japan (Kobayashi, 1979; Nakashita et al., 1988; Shibano et al., 1989), Europe (Je€ery et al., 1987; Karlsson, 1988) and the United States (Rillings, 1991). With the rapid evolution of ITS and emphasis placed on real-time trac management, dynamic route guidance systems, and dynamic trac assignment systems, further development and re®nement of ATIS and has occurred during the 1990s. The era of development of traveler information systems can be grouped into two distinct classi®cations. First generation systems, which we refer to as traveler information systems, arose from the emergence of computer technologies and trac surveillance and control systems in the late 1960s and early 1970s. They represented an initial attempt to use communication technologies for information dissemination. In many cases, these systems were designed to improve ¯ow at localized points in a network, such as a heavily congested freeway-to-freeway interchange, or to make travelers aware of non-recurring congestion, such as special events or incidents. Variable message signs (VMS) and highway advisory radio (HAR) are representative 1st generation systems. We are currently experiencing the growth and maturity of what could be termed 2nd generation systems, or ATIS. Today's ATIS encompass a wide range of new technologies and are being designed to provide travelers with dynamic route guidance, real-time trac condition information and traveler

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services information. Metropolitan areas are looking toward regional multimodal traveler information systems to enable the traveling public to make more informed mode and route choices. The leap from 1st generation to 2nd generation information systems represented a signi®cant change in philosophy over what traveler information is and how it is presented. VMS and HAR are one-way communication systems which are used to convey general traveler information to vehicles and the responsibility fell upon the user to sift through the information to determine what, if any, part of the broadcast applies. Second generation systems attempt to personalize the provision of travel assistance. The advances in communication, mapping, and multimedia technologies have led to systems which can provide a speci®c traveler with routing, way®nding, or yellow pages assistance. Second generation systems eliminate data sifting by reducing the amount of information provided to the traveler and focusing on the speci®c needs of that user. Characteristic 2nd generation communication devices include IVRGS, cellular telephone, cable television, information kiosks, and the internet. Second generation systems are capable of more direct broadcast of information to users by incorporating one or more of the following ``advanced'' properties: Interactive user interface: ATIS provide an opportunity for quasi two-way communication as users can request speci®c information from the system. For example, in-vehicle systems allow drivers to specify a destination and the system can compute a shortest path route for the driver. This interaction between user and machine may be facilitated through multilingual and menu-driven interfaces and multimedia presentation consisting of both visual and auditory exchanges. Vehicle location and intelligent mapping: Many ATIS integrate highly de®ned mapping with Global Positioning Systems (GPS) to enable real-time vehicle tracking. These systems are capable of positioning the vehicle, determining if the vehicle is on or o€ course, and making appropriate adjustments to routing strategies to help the traveler navigate through the network to the intended destination. Individualized path search: Several routing systems allow users to select from one of several travel objectives used to direct the path search. Typical options include minimizing travel time, minimizing travel distance, and maximizing use of freeways links. Yellow pages directory: Some ATIS come with pre-programmed directories of major attractors, retail stores, restaurants, hotels, and other destinations that drivers might want to locate. These systems can help travelers select a destination in conjunction with routing options. For example, users could query the system to locate the nearest movie theater or to ®nd a certain type of restaurant within a 15 min travel time radius. Multimodal information: Smart information kiosks have begun to incorporate information from both highway and transit systems to provide comprehensive travel planning including mode, departure time, and route choice. Dynamic route guidance (DRG): Dynamic route guidance systems are being designed to provide route recommendations based on actual or predicted trac conditions based on data gathered from an equipped

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network. Watling and Van Nuren (1993) provide a comprehensive discussion of DRG including a comparison of DRG systems to 1st generation systems, such as VMS. Second generation ATIS are still under development and have not yet achieved their projected level of market penetration. However, while there is little doubt that more of these systems will come on-line in the near future, there are some lingering questions regarding the e€ectiveness of these systems that must be addressed. Bar®eld and Mannering (1993), in their overview of a special issue of this journal [Transport Research 1C (2)] dedicated to ATIS, suggest that there are ®ve basic behavioral and human factors issues in ATIS that warrant study: (1) Do travelers use ATIS? (2) How and when do travelers use ATIS? (3) Why do travelers use ATIS (4) How do travelers perceive ATIS? (5) What are the consequences of ATIS? Over the past 5 years, a plethora of papers have been published in response to these ®ve issues. The following is a brief listing of relevant work, many of which have been published in this journal. Khattak et al. (1993) and Adler et al. (1993) looked at enroute behavior and implications for ATIS development. Driver preference for navigational information and ATIS was addressed by Mannering et al. (1995) and Wochinger and Boehm-Davis (1997). Thakuriah and Sen (1996) look at the e€ect of information quality on ATIS usage. The e€ects of ATIS on network performance is discussed by Jayakrishnan et al. (1993) and Hall (1996). Designing route guidance systems to address behavioral and human factors concerns have been discussed by Dingus and Hulse (1993) and Ross et al. (1997) among others. Beyond the ®ve fundamental issues regarding ATIS development presented by Bar®eld and Mannering, there are several additional important issues that have an impact on drivers who would consider investing in current ATIS technologies: Cost: Traveler information systems are being developed as services to travelers and the users are expected to pay for the service (either by purchasing an in-vehicle system or subscribing to a regional information provider). Currently the cost of in-vehicle devices is high and relatively few drivers have opted to purchase these devices. In addition, 2nd generation ATIS that provide real-time information or dynamic route guidance rely on the availability of data. A well-equipped intelligent transportation infrastructure is needed and also must be paid for. Compliance: Compliance refers to whether drivers follow the advice being provided via ATIS. Drivers who believe that the information being provided is not accurate, misleading, or not useful would be less likely to comply with the system. In the longer run, if driver con®dence in these systems fails, then fewer people will purchase these systems and the expected bene®ts will not be realized. Oversaturation: Several studies have indicated that as the number of equipped vehicles increases, the marginal bene®t to the network diminishes. Some have shown that maximum system bene®t is achieved if only a small portion of drivers (up to about 25%) is equipped. Mahmassani and Chen (1991) assert that ultimately, the decision for a traveler to invest in ATIS will be based on the trade-o€ between system cost and perceived bene®ts. If the system experiences diminished value from ATIS as market penetration increases, then fewer travelers may be tempted to invest in these systems.

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E€ects over the long-term: There is great speculation of what impacts ATIS will have over the long run. Watling and Van Nuren (1993) suggest that there is a potential for signi®cant changes in travel demand including increased use of public transportation, increased demand for travel, changes in activity patterns. However, few studies have been able to predict how consumer use of ATIS will evolve over the long run. Adler et al. (1993) suggest that the need for information changes as travelers become more informed about a network. It is therefore possible that drivers investing in a certain type of ATIS today might require di€erent services in the future. Jackson (1994a) questions the long-term reliance of drivers on real-time information. He states that continued use of ATIS does not simply depend on the provision of real-time information; rather, usefulness will also depend on a number of perceptions including time savings, reduction in driving/decision making e€ort, and psychological e€ects. Driver comfort: Drivers will be more apt at investing in in-car systems if the interface and operation are easy to handle. In addition, drivers seek systems that respond in a manner consistent with the driver's behavior, but sooner, smoother, and in a more reliable manner. Perel (1998) suggests that comfort and trust in the system's ability to compliment driver behavior is an essential criteria for older drivers whom may consider investing in these systems. For ATIS to become more widespread and a permanent feature in the marketplace the technology must be a€ordable, provide understandable and believable information, be perceived as providing a personally valuable service, and complement within-day and day-to-day traveler behavior. To reach this goal, ATIS must be exceptionally user-friendly and capable of providing customized and personalized travel assistance. It is postulated that the movement toward intelligent traveler information systems would alleviate many of the concerns regarding current ATIS. The aim of this paper is to show how machine intelligent systems are a natural outgrowth of current technologies and can be achieved through the integration of ATIS and arti®cial intelligence methods. The format of the paper is as follows. The next section presents background into the relationship between ATIS and travel choice behavior and outlines some of the design and behavioral issues that are of concern in the context of providing drivers with useful information and increasing market penetration of these systems. This needs assessment is followed by discussion of arti®cial intelligence (AI) methods that could be applied to improve in-vehicle traveler information quality and utility, which we think, could lead to a more driver-speci®c and useful approach to ATIS and a more marketable technology. 2. ATIS and travel choice behavior Understanding traveler behavior is an important consideration for developing and marketing traveler information systems. How people make travel choices (including destination, mode, departure time, and route) is a re¯ection of their knowledge of the traveling environment and their personal preferences for and needs toward travel. In a world where travelers have perfect information regarding current and anticipated travel conditions in the network, spatial orientation of the network, and routing and mode choice options there would be little need for traveler

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information systems. In this perfect scenario travelers would be able to successfully plan their travel and navigate through the network with ease. However, since we live in a world where travelers have less than perfect knowledge about the network conditions and travel options and travel can evoke much anxiety and stress, traveler information systems are a welcomed technology. Enabling these systems to best satisfy the needs of travelers is paramount to ensuring that travelers will invest in and comply with them. According to Jackson (1994a,b) there are three primary issues that must be addressed with respect to ATIS and driver behavior: 1. To understand the e€ect of ATIS and information processing on routing behavior we need to better understand the processes by which a driver seeks to acquire and use spatial knowledge under normal conditions (without ATIS), 2. Will ATIS, particularly route guidance systems, expand or hinder the development of drivers' cognitive representations of the spatial environment? 3. The characteristic, which may have the largest in¯uence on a driver's decision whether or not to accept and rely on externally provided information, is the perception of the usefulness of ATIS as a driving aid. It is therefore critical that these systems can re¯ect driver behavior and the need for information acquisition and processing and provide information that is perceived as being personalized, timely, and relevant. This section presents a broad overview of research on driver behavior and identi®es some of the issues that are being addressed in the context of understanding driver behavior and the e€ect of traveler information systems. In recent years there has been a signi®cant increase in research aimed at understanding routing, way®nding, and navigation behavior and the role that information systems could play in providing travelers with needed information. The following general thrust areas can classify these e€orts: 1. Determining traveler preferences for ATIS including type of information, type of display, and presentation media. 2. Understanding and modeling route choice/switching behavior focused on the factors and needs that in¯uence the decision processes. 3. Representing and modeling cognitive processes, including spatial cognition and cognitive maps, that a€ect routing behavior and the need for information. 4. Assessing and evaluating the e€ects of ATIS on network performance using trac simulation. 5. Conceptualizing dynamic models for analyzing the interaction between traveler behavior and ATIS. Conceptual models of driver's behavioral choice under information have been proposed by several researchers including Ben Akiva et al. (1991); Adler et al. (1993), and Khattak et al. (1993). Consistent across these models are certain aspects of the process that must be accounted for. First, there exists a signi®cant di€erence between pre-trip planning and enroute switching choice domains. Pre-trip planning de®nes a static choice scenario in which mode, route, and departure time decisions are made against a set of alternatives. Enroute switching is a dynamic choice process in which a driver constantly compares the performance on the current path to a set of expectations and is motivated to switch paths when some threshold of expectation is not being achieved.

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Second, choices are in¯uenced by a combination of historical experiences, perceptions, and externally provided information. The perceptions of the network orientation and dynamic conditions in¯uence travel choice decisions. Historical experiences gained through previous trip making shape a driver's level of understanding of the network and the travel patterns as well as re¯ect his learning capabilities. Current perceptions are a function of the traveler's experiences on the network during a given day. ATIS is the source for externally provided information. Last, day-to-day variations in travel performance and maturing of driver attitudes and perceptions shape driver behavior and in¯uence travel decisions. Much of this is dependent on the driver's ability to learn about the network both from personal experience Bovy and Stern (1990) and from information made available through ATIS. In general there are three sources of information that are used by travelers to make routing decisions: (1) historical experiences gained through the learning process of previous trips; (2) current perceptions of the network's state on a given day; and (3) external information acquired via ATIS. There is an important distinction made between driver behavior as it relates to within-day decision making and between-day dynamics. The within-day problem focuses on the travel choices made by an individual for a speci®c travel need at a given time on a given day. The travel objectives, travel needs, perceptions, behavioral tendencies, and cognitive abilities that in¯uence this choice process are re¯ective of the state of those variables at the instant when the choice is being undertaken. More robust models of driver behavior under ATIS are being developed to represent within-day pretrip route selection and enroute path switching. Examples include the boundedly rational model proposed by Mahmassani and Stephan (1988) and the con¯ict model proposed by Adler et al. (1993). The dynamic formulation is concerned with modeling how the state of the network changes from day-to-day and evolves over time. In addition, the spatial knowledge of a driver is constantly evolving in response to travel made through the network. The lack of understanding of how spatial knowledge in¯uences route choice and the need for information contributes to the diculty in modeling the dynamics of driver behavior and developing ATIS that will best meet the needs of the traveling public. There are three speci®c issues regarding spatial cognition and route choice that are of special interest: 1. Mental representation of a network's spatial orientation: Routing between two points in a network depends on knowledge regarding how streets are linked with each other. Very detailed maps can provide travelers with this information. Without the use of maps, this knowledge is learned by making repeated trips through the network and developing a mental representation (mental map) of the network. Wenger et al. (1990) discuss the notion of cognitive maps with respect to motorist decision making and the design of information systems. 2. Understanding of the network's stochastic nature (dynamic conditions): Networks experience variations in trac patterns over the course of a day and each day presents a di€erent set of conditions. Drivers who make repeated trips through the network learn to better understand these variations and are able to make more informed and intelligent travel choices. Inexperienced travelers with ATIS trac condition information undergo new learning dynamics in assessing travel conditions and in making better travel decisions. 3. Evolution of individual behavioral tendencies: Over time and with experience, there is a tendency for people to mature and their behavioral tendencies to change. With respect to routing behavior, some of the change in attitudes stems directly from trip making experience.

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3. The need for intelligent systems Just as the leap from 1st to 2nd generation information systems represented a signi®cant shift in philosophy, we believe that a similar shift will occur as ATIS technologies become more personalized and intelligent. We assert that the next great leap in traveler information systems will occur as a result of the integration of Arti®cial Intelligence with ATIS leading to the creation of 3rd generation systems, Intelligent Traveler Information Systems (ITIS). We believe that the jump to 3rd generation systems is critical in ensuring the long-term success of traveler information systems in the marketplace. The need for more personalized and intelligent systems can be understood with an analogy to the service industry. It is well known in the service industry that when people purchase a service, whether buying food at a restaurant, seeing a doctor, or using a travel agent for booking a trip, repeat business is dependent on the perception by customers that their needs are being met and that the server is highly responsive. Customer service orientation is considered essential to success in business in today's marketplace. By analogy, new technology in the marketplace will be more competitive and successful if it is customer-oriented and smart enough to be customer speci®c. In extending the smart concept in electronics to an AI-based intelligence that can learn a user's pro®le of interests, a new standard of performance in information provision can emerge. One of the major hurdles for 2nd generation ATIS is providing travelers with personalized service. Consider today's IVRGS that require users to input basic information during each trip. Before each trip to be made the driver enters the vehicle and informs the system of the desired destination. In response to the request, the system generates a best path and guides the driver to the destination. This same pattern is followed for each successive trip, regardless of the driver or trip purpose. This IVRGS clearly lacks real intelligence in that it has no ability to remember previous interactions with the driver. It is like a service provider you interact with everyday, but who begins from ®rst premises in dealing with you, every time. We question whether consumers, in a di€erent context, would be satis®ed to pay for this kind of service. For example, would a traveler continue to use a travel agent who, after booking several trips with this person, still could not recognize you when you walked in the door or who did not know your travel preferences (such as what constraints you place on your travel including the airline you prefer to ¯y or whether an aisle or window seat is preferred or whether you require a special meal). When people use a service on a regular basis they expect a certain level of response from the server. People seek out someone whom they can rely on, whose judgments they can trust, and someone who can best take care of their needs. When costs are equal, more personalized service is more marketable and competitive. In comparison to the description of IVRGS provided above, consider an interaction with an intelligent in-vehicle system. Upon entering the vehicle and communicating with the IVRGS, the system recognizes that you are the driver. Based on the time of day and day of week your trip purpose is anticipated. If you enter your car at 7:30 am on a Monday the system anticipates that you are making a work trip and asks you if you intend to make your regular trip to work. For trips that are repeated regularly, such as journey to work, the system would infer your intended destination, desired travel objectives, and preferred usual route. Reaching into its historical database and interfacing with existing information providers in the instrumented network the system quickly retrieves information regarding current and historical travel conditions to inform you if your usual path is operating

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within expected conditions. In essence, this intelligent system would be capable of recognizing travel patterns and route preferences for each user, be able to evaluate network conditions with respect to these preferences, and carry on a dialogue with the user to provide personalized route guidance. We believe that this ``futuristic'' vision will be the natural progression from current e€orts in personalized ATIS. Currently, developers are well aware of the opportunities for providing personalized ATIS. In Boston, Smart Route Systems has cellular phone subscribers who receive en route travel updates based on the travel pro®les established when registering for the service (Englisher et al., 1997). Smart Route Systems has actively sought to expand their coverage in major cities including Washington DC and New York City, where they are participating in the New York Model Cities Deployment Initiative project. A similar fee-based multimodal ATIS is planned and a key component of this system will be personalized pro®ling. The service provider will create a pro®le of the driver including destinations and routes used. When network congestion or incidents arise in the network that might a€ect a registered user's trip, the provider will contact the user and provide personalized real-time trip information. The ADVANCE ITS demonstration project in Chicago has reported results on driver preferences (Schofer et al., 1997) and the e€ectiveness of ATIS (Saricks et al., 1997) with dynamic route guidance systems. Focus groups of users reported on the importance of 19 features of a realtime in-vehicle DRG tested in the project. The test drivers wished to have more control over the route planning and to set their own criteria. The drivers suggested that computer learning of their route selection criteria or of their favorite routes would be desirable. The study further suggested using the ``computer as an intelligent assistant to the driver-expert'' (Schofer et al., 1997, p 9). It is posited that market forces will promote the most intelligent ATIS, those with increasing degrees of personalized travel assistance. The following sections discuss the merging of ATIS and machine intelligence in the development of a Intelligent Traveler Information Systems (ITIS). It begins with a discussion of arti®cial intelligence and prescriptions for applying some state-of-theart arti®cial intelligence to ATIS. The description of the needs assessment and possible applications of AI for ATIS technologies is then presented. 4. Arti®cial intelligence and ATIS Creating intelligent systems to enhance travel is at the heart of ITS. In the Summary Report of Phase I of the ITS Architecture Development Program (USDOT/ITS, 1994) intelligence ``refers to processing and communications power, to di€erent ®xed and mobile elements of ITS. . .centralized approaches allocate the intelligence within a few components. A distributed, or decentralized, approach spreads intelligence over many elements.'' According to this report, the term intelligence is being used to describe technological power. This use of intelligence di€ers from a more conventional approach as has been applied to computers and technologies. Intelligence typically refers to cognitive skills and making inferences. Webster's New World Dictionary of Computer Terms (Spencer, 1992) provides the following de®nition of arti®cial intelligence: arti®cial intelligence (AI): a group of technologies that attempt to emulate certain aspects of human behavior, such as reasoning and communication, as well as to mimic biological senses

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including seeing and hearing. Speci®c technologies include expert systems, natural language, neural networks, machine translation, and speech recognition. AI involves using computers and software that, like the human mind, use stored knowledge to make decisions involving judgment and ambiguity. We believe that there should be a natural link between the ®eld of arti®cial intelligence and the development of traveler information systems. AI aims to emulate certain aspects of human behavior; traveler information systems are designed to support traveler decision-making. Understanding how people make travel-related decisions and what information is necessary to make this process more ecient can help in the development of ATIS. Real intelligence extends beyond the power of communications and processing to emulate human behavior and make decisions. It can be argued that many current ATIS involve some level of intelligence. For example, systems that can prescribe routing strategies by combining vehicle location, mapping, and search algorithms have intelligence, albeit at a relatively low level. Higher intelligence occurs when technologies are built that can incorporate self-diagnosis and learning based on past actions to improve their performance. In ITS this translates into machine learning technologies that capture the dynamics of travel choice behavior. It is suggested that while not every ATIS needs to be intelligent, or have the same capabilities, there could potentially be great bene®t to the traveling public in having intelligent systems. In®nity2, the car manufacturer, boasts in one of their commercials for their Q45 automobile, an ``intelligent'' system that allows owners to preprogram the driver-side seat and steering wheel positions for multiple drivers so that with a press of a button the cockpit will adjust to suit the driver's preferences. This is similar to the in-vehicle route guidance system that we described earlier that can be responsive to the needs of di€erent drivers based on di€erences in personality, travel behavior, and preferences. Clearly, all travelers would not require the same services of intelligent systems. For instance, tourists in need of traveler information services or basic directions to a destination would not bene®t from the same intelligence as commuters. The bene®ts for intelligent systems are most easily gained for those people making repetitive trips, though it is conceivable that intelligent tourist guides or business traveler guides would use many of the same data object classes that commuters would, albeit with more pre-trained data based on the behavior of speci®c user groups. At this stage more emphasis is placed on the learning of commuter trips since they are repetitive, and thus more easily learned. A driver could rely on this system to learn, through repetition in trip making, her preferences for routing under variations in trac ¯ows across the peak period and under varying weather conditions. 5. AI technologies and development of intelligent systems 5.1. Basic activities of ITIS ITIS may be developed in many ways and will vary in the capabilities and features that reach the marketplace. ITIS are envisioned to be very serviceable and adaptable to individual users'

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needs and behaviors. It is suggested that the basic activities and the most reachable in the shorter term would include the abilities to: 1. Recognize the driver: Each driver has personal travel objectives, driving habits, understanding and perceptions of the network. The route guidance system stores user-speci®c information that is recalled when a user ``logs in'' to the system. 2. Communicate with the driver: The user interface de®nes the way in which the driver interacts with the route guidance system. Basic systems are driven by manual manipulation that requires the driver to push buttons on the display and receive directions visually through the monitor. Smarter systems have integrated auditory interfaces in which drivers verbally address it and receive auditory navigation instructions. 3. Learn driver preferences: Either implicitly by studying historic trip making behavior or explicitly by asking drivers to rate their perception of the travel experiences, the route guidance system learns each driver's routing preferences, even in a multiple-objective framework. Still more intelligent systems distinguish between trip types and generate routing options based on the driver's preferences and trip type. 4. Interface with various real-time information sources: The value of the link and node attributes for the network will in¯uence the path selection process. Currently many systems rely on spatial databases (e.g. those stored on CD) and rely on free ¯ow values for travel times (from speed limits). In an environment where real-time information, including travel conditions and weather, is available, route guidance systems search the information servers that are on-line, probably by radio transmission, to develop a travel itinerary to meet the driver's personal objectives. 5. Minimize potential safety problems: Human factors studies have cautioned that in-vehicle systems have the potential to distract the driver from the driving task by shifting attention from the roadway to the device. Intelligent route guidance systems should be designed with ergonomic and safety principles and interface with other in-vehicle control and warning systems, such as CWS and AHS, to warn drivers when the communication is becoming a potential safety hazard. Autopilot modes might protect the driver from information overload. Many of these basic activities can be achieved through the integration of AI and ATIS. The following sections suggest areas where AI can contribute to the inclusion of driver preferences in route selection, communication, and trip quality. Ultimately, this may result in a more driverspeci®c and useful approach to ATIS and a more marketable technology. 5.2. Natural language processing E€ective communications between the traveler and the route guidance system is one of the most important attributes of any route guidance system. It is critical to enable drivers to easily convey their needs to the system so that the path that best meets the driver's needs can be found. Alternatively, it is critical that the system communicate route guidance instructions so that drivers can comprehend the instructions and follow a route safely without error (Means et al., 1993). The manner in which the instructions are presented by the system has a signi®cant impact on a driver's navigational abilities and con®dence. It is common for travelers to use di€erent approaches to verbalizing route guidance. Some people are interested in knowing street names, others prefer

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landmarks. In some cases, knowing how far to travel on a path before turning is important. Consider the following two instructions: ``Travel two blocks then turn right on Main Street'' and ``Continue straight for 1/4 mile and turn right at the Mobil station.'' Although, both instructions indicate that a driver should move ahead on the current street for a distance and then make a right turn, di€erent drivers may prefer one approach to the other. Providing auditory instructions that are unambiguous and easily understood by drivers is a challenge for developers. Similar linguistic issues apply in terms of providing drivers with auditory trac advisories. Means et al. (1993) explain that there are many ways to express severity of congestion but little is known about what terms are preferred by drivers. The communication needs for traveler information systems seem well suited for natural language processing systems. The purpose of natural language processing is to enable computers to understand language. A user interface that can interpret verbal instructions from the user, recognize intent, and respond intelligently would demonstrate understanding. The challenge for developing natural language processing systems include facilitating inferences, recognizing ambiguity and intent in language, supporting a large vocabulary and knowledge base that includes popular jargon, and having a clear representation of meaning. While we have not yet reached the point where we have systems that can carry an intelligent conversation, we are witnessing route guidance systems, like TravTek, that have started to implement auditory interfaces for both driver to system and system to driver communications. Steve Wollenberg, President of Fastline, stated in an interview for Trac Technology International regarding in-car computers that voice recognition technologies are reliable but ``the remaining challenge is to improve speech recognition software so that it will recognize voice commands in noisy environments'' (Trac Technology International, April/May 1998). A well-designed natural language processing system would provide a safe driver±machine interface by removing the distractions of visual displays and reducing vagueness in procedural instruction. Moreover, natural language processing could promote responsiveness to driver needs in competently understanding requests and driver instructions. 5.3. Machine learning The development and integration of more complex and complete models of driver behavior for route guidance systems is an important research component of ITIS. As stated earlier, a driver's cognition of a road network has signi®cant in¯uence on path selection. Because drivers are constantly learning and updating their perceptions of the network, driver behavior is also constantly evolving. Two aspects of path selection are dynamic in nature. First, spatial knowledge of the network e€ects a driver's recognition of the existence of alternative paths between any two points. For example, if a driver has only traveled on the freeways he may not know that certain arterials will also lead to an intended destination. It is also likely that this driver would be more comfortable with the freeway path options and reject a routing advisory from a guidance system to take a surface street path. Second, because path selection is based on multiple criteria, an oversimpli®ed, single-objective path choice process within the route guidance system would also lead to paths choices that a driver would may choose to reject. Over time, when multiple instances of a trip are made, a route guidance system equipped with some learning program should be able to develop a characterization of the drivers' preferences.

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This in turn should enable the system to ®nd paths that best meet the driver's needs. In terms of natural language processing, through feedback, the program should learn which types of instructions the driver prefers. Machine learning would also be useful in terms of the routing system becoming more informed about travel conditions in the network. On the basis of repeated trips, a learning program would be able to recognize patterns of travel congestion, distributions of travel times, probability of incidents, and other link, path, or node speci®c attributes that could be used to improve network search and path selection. It is likely that learning could be accomplished through neural network techniques or fuzzy adaptive systems. 5.4. Approximate reasoning/fuzzy logic Uncertainty is a critical issue in driver behavior and path selection. Path selection and network search can involve lots of data, both historical and currently prevailing. In instances when a complete set of information is unavailable there is additional uncertainty regarding parts of the network. From the perspective of driver behavior and path preference, in the case of multiple criteria in¯uencing path choice there are also areas of uncertainty where the driver cannot perfectly explain his needs and the system must infer these objectives from previous trips. AI techniques that are good at handling uncertainty, such as approximate reasoning or fuzzy logic models could help improve communications between the driver and the system and the path search logic employed by the system. Over the past few years there have been several attempts to incorporate fuzzy logic for modeling route choice behavior and designing route guidance systems. Lotan and Koutsopoulos (1993) and Adler and Casello (1998) describe approaches for using fuzzy logic to model driver path choice. Pang et al. (1995) describe a fuzzy-neural approach to represent the correlation of the attributes with the driver's route selection. Approaches like these could be integrated into ATIS to model driver routing preferences and handle uncertainty. 5.5. Heuristic search Finding paths through a network in real time has evolved as a formidable challenge to ITS developers. A central component of ATIS and DRG systems is an ability to prescribe ``optimal'' paths for drivers from their current location to a desired destination. There are several classi®cations of problems relating to ®nding minimum paths between a given origin-destination pair that are of interest to developers of route guidance and intelligent transportation systems. These problems include the static minimum path problem, time-dependent minimum path problem, stochastic minimum path problem, and k-minimum path problem. For large urban networks with thousands or nodes and links, the computation of shortest paths can become computationally burdensome. Finding alternative formulations of path ®nding algorithms that provide ``good'' approximations of the optimal solution while signi®cantly reducing execution time is of great motivation to researchers. Heuristic improvements to path algorithms are particularly well suited for transportation networks and route guidance systems. In particular, the A* algorithm, attributed to Hart et al. (1968) is very successful in reducing the computational e€ort associated with ®nding minimum or K-minimum paths on euclidean

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networks (Golden and Ball, 1978; Sedgewick and Vitter, 1986; Skiscim and Golden, 1987; Rilett et al., 1994). Several algorithms are available to ®nd optimal paths in a network. Divoky and Hung (1990) and Cherkassy et al. (1996) provide a comprehensive overview of shortest path algorithms and their computational performance. However, in many cases, it is not necessary to identify optimal paths but to identify alternatives that satisfy the traveler's objectives. Scott et al. (1997) describe an approach to generate alternatives to the best path. As work on DRG and DTA evolve, it has become evident that path search should not be limited to single objective approaches and that algorithms are needed to extend the e€ort to handle multiple objective search. Blue et al. (1997) describe an algorithm that can perform multiple objective search and trade-o€ criteria between competing objectives, such as travel time and trip complexity. Traditionally, algorithms to solve multiple objective search problems are cumbersome. Heuristic functions may provide opportunities to improve the running time of these more extensive algorithms. A multiple objective A* approach was introduced by Stewart and White (1991) but they conceded that these search routines will be very computationally intense. It was suggested that further study to develop inadmissible heuristics might be of practical advantage. 6. Conclusions and recommendations Advanced Traveler Information Systems have emerged as a key component of ITS and trac management systems. Today's route guidance systems are capable of assisting drivers to navigate through a network. It is suggested that while current route guidance systems are designed to help a speci®c traveler with routing, way®nding, or destination (through an in-vehicle yellow pages directory) assistance, these 2nd generation ATIS are advanced but not really intelligent. It is envisioned that 3rd generation traveler information systems that combine current ATIS with arti®cial intelligence methods would greatly enhance the viability and marketability of route guidance systems. The objective is to create ITIS that can more e€ectively respond to drivers' within day path selection needs and can adapt to changes in drivers' cognition, spatial knowledge, travel preferences and attitudes over time. Natural language processing, machine learning, approximate reasoning, and intelligent search are among the AI techniques that could be e€ectively incorporated, leading to the development of machine intelligent routing assistants.

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