Eran Ben-Elia

Information Impacts on Route Choice and Learning Behavior in a Congested Network: Experimental Approach

Every traveler makes route choices in an uncertain environment that includes random disruptions to the traffic system such as incidents, bad weather, and random behavior of fellow travelers. The premise underlying the development of advanced traveler information systems—that better-informed travelers make better route choices—should be tested. This paper studies en route real-time information about the occurrence of an incident and ex post information on forgone payoffs (FPs) (i.e., travel times on nonchosen routes). Data were collected from an interactive experiment in which subjects made multiple rounds of route choices on a hypothetical network subject to random capacity reductions, and travel times were determined by performance functions of route flows from the previous round. En route real-time information increased the networks travel-time savings and reliability under the experimental setting, yet FP information had the opposite effect. The most efficient information structure in terms of travel-time savings is a combination of real-time information and no FP information. Real-time information at downstream nodes encourages participants’ strategic behavior at the origin. FP information appears to increase risk-seeking behavior; it encourages route switching without real-time information and suppresses it with real-time information. These results could be valuable for policy evaluations of further developments of advanced traveler information systems.

Response to Travel Information: A Behavioural Review

Abstract Innovation in information and communication technologies (ICTs) is providing us with a myriad of travel information sources. Knowledge on the influence of information on human travel behaviour (mainly route and mode choice) and their implications on network levels of service remains fragmented. We distinguish between experiential, descriptive, and prescriptive information sources. We draw on recently developed theoretical concepts in behavioural and cognitive sciences to examine the state of the knowledge on information and travel behaviour. Key theoretical concepts used to explore the relationship between information and travel behaviour include: reinforced learning; framing; risk and loss aversion; probability weighting; affect; anchoring and ambiguity aversion; and regret aversion. We review studies focusing on individual travel behaviour as well as network studies involving collective behaviours. While information seems to assist individual travellers in coping with uncertainty, the impacts relating to collective behaviour on networks remain unclear. Many open questions remain, yet research provides important insights and suggests that ICTs will enable the design of persuasive information systems that motivate cooperative and efficient use of the transportation network beyond what is possible today.

Travelers' day-to-day route choice behavior with real-time information in a congested risky network

Nonrecurring disruptions to traffic systems caused by incidents or adverse conditions can result in uncertain travel times. Real-time information allows travelers to adapt to actual traffic conditions. In a behavior experiment, subjects completed 120 “days” of repeated route choices in a hypothetical, competitive network submitted to random capacity reductions. One scenario provided subjects with real-time information regarding a probable incident and the other did not. A reinforcement learning model with two scale factors, a discounting rate of previous experience and a constant term, is estimated by minimizing the deviation between predicted and observed daily flows. The estimation combines brute force enumeration and a subsequent stochastic approximation method. The prediction over 120 runs has a root mean square error of 1.05 per day per route and a bias of 0.14 per route.

Prospect Theory and its Applications to the Modelling of Travel Choice

Abstract Purpose This chapter explores Prospect Theory — a descriptive model of modelling individual choice making under risk and uncertainty, and its applications to a range of travel behaviour contexts. Theory The chapter provides background on Prospect Theory, its basic assumptions and formulations, and summarises some of its theoretical developments, applications and evidence in the field of transport research. Findings A body of empirical evidence has accumulated showing that the principle of maximisation of expected utility provides limited explanation of travel choices under risk and uncertainty. Prospect Theory can be seen as an alternative and promising framework for travel choice modelling (although not without theoretical and practical controversy). These findings are supported by empirical observations reported in the literature reviewed in this chapter. Originality and value The chapter provides a detailed account of the design and results of accumulated research in travel behaviour research that is based on Prospect Theory’s observations, insights and formulations. The potential of Prospect Theory for particular decision-making in travel behaviour research is articulated, main findings are presented and discussed, and limitations are identified, leading to further research needs.

Emergence of System Optimum: A Fair and Altruistic Agent-based Route-choice Model☆

Abstract The System Optimum, an optimal traffic assignment that minimizes the total travel costs on the road network is usually only referred to as a comparison to self-emerging user equilibrium. In this paper we investigate how different behavioral aspects of drivers can self-organize towards a system optimum that minimizes travel costs while providing benefits and preserving equity among drivers. We present a simple binary route-choice Agent-Based Model that provides a disaggregated view of driver behavior and a unique understanding of the potential of cognitive reinforcement models to effect a convergence to user equilibrium and a shift in driver behavior toward a system optimum without the need for an enforcing traffic policy such as tolls.

The benefits of a high-resolution analysis of transit accessibility

ABSTRACT Accessibility is an important consideration in sustainable mobility policies, particularly for transit users. Measures suggested in the literature are based on coarse aggregate spatial resolution of traffic analysis zones that is sufficient for managing car travels only. To reflect a human door-to-door travel, transit accessibility demands an explicit view of the location of origin, transit stops and destination, as well as of the temporal fit between transit lines timetable and traveler’s needs. We thus estimate transit accessibility based on mode-specific travel times and corresponding paths, including walking and waiting, at the resolution of individual buildings and stops. Car accessibility is estimated at a high resolution too. A novel representation of transit network as a graph is proposed. This representation includes all modal components of a transit travel – walking, waiting at a stop, transit ride, transfers between lines, thus enabling unified view of a travel, regardless of mode. The use of modern high-performance graph database allows construction of high-resolution accessibility maps for an entire metropolitan area with its 100–200 K buildings. The application is tested and applied in a case study involving the evaluation of the 2011 bus line reform in the city of Tel Aviv. Specifically, we demonstrate that while the reform increased the average accessibility for the entire city the increase was not uniform with different areas of the city experiencing different absolute accessibility by transit and relative accessibility in comparison to car travel. The bus reform did in fact benefit travelers that experienced low relative accessibility, but the benefits are mainly accruing to longer trips. Our approach and computational methods can be employed for directly investigating the impacts of transportation infrastructure investments.

Transportation Impact Statement (TIS)—A New Tool for Transportation and Land-Use Planning

The authors advance a new approach to transportation and land-use planning: the transportation impact statement (TIS). Current planning practice suffers from a lack of understanding of and adequate tools to evaluate the complex relationships that exist between land use and transportation. Consequently, land-use development frequently overloads the transportation system. A TIS exposes the complex interdependencies with a multimodal and regional assessment of the impact of land-use development on the transportation system. The authors offer a theoretical background for this new approach and an empirical illustration of its potential use through a case study based on the city of Haifa in Israel. The objective of the study is to investigate the local and regional transport-related impacts of proposed land developments, thus improving the planning decisionmaking process. The impact of the proposed land developments on the transportation system was studied utilizing several transportation scenarios, including travel-demand management (TDM) strategies, using the metropolitan database and travel-demand modeling systems. The results show that the total number of trips generated by the proposed land developments is by far inconsistent with the capacity of the transportation network to accommodate all the forecasted demands under all transport scenarios. These results have a number of implications. First, TIS clearly improves our understanding of the impact of land development on the transportation system, and thus it should be utilized in decisionmaking regarding land-development strategy. Second, TIS stresses the importance of transit and TDM strategies as mitigation measures in the planning process. Third, TIS illustrates the need for a wider (that is, not site-related) planning perspective—including setting overall metropolitan goals and objectives.

An Agent-Based Model of a System-Optimal ATIS

Abstract: System optimum is usually referred to as a theoretical traffic assignment, whose main use is comparison to user equilibrium. In this paper, we investigate an advanced travel information service (ATIS) that provides the travelers system optimal routing signals, so that if all travelers comply with the signal, system optimum is achieved. We present a simple binary route-choice Agent-Based Model that includes the interaction between agents in multiple congestion sensitive road networks, under different allocation of routing signals. We find that the frequency agents receive a better signal and the allocations used by the system have a great effect over the road network convergence to system optimum. The contribution of the findings enables a great reduction in aggregate network travel time only through a behavioral change to the agents.

Estimation of Urban Transport Accessibility at the Spatial Resolution of an Individual Traveler

Accessibility, particularly for public transport users is an important consideration in sustainable mobility policies. Various accessibility measures have been suggested in the literature, most at coarse aggregate spatial resolution of zones or neighborhoods. Based on recently available Big Urban GIS data our aim is to measure accessibility from the viewpoint of an individual traveler who traverses the transportation network from one building as origin to another at the destination. We estimate transport accessibility by car and by public transport based on mode-specific travel times and corresponding paths, including walking and waiting. A computational application that is based on the intensive querying of relational database management systems is developed to construct high-resolution accessibility maps for an entire metropolitan area. It is tested and implemented in a case study involving the evaluation of a new light rail line in the metropolitan area of Tel Aviv. The results show essential dependence of accessibility estimates on spatial resolution—high-resolution representations of the trip enable unbiased estimates. Specifically, we demonstrate that the contribution of the LRT to accessibility is overrated at low resolutions and for longer journeys. The new approach and fast computational method can be employed for investigating the distributional effects of transportation infrastructure investments and, further, for interactive planning of the urban transport network.

Impacts of Incentive-Based Intervention on Peak Period Traffic: Experience from the Netherlands

Incentive-based travel demand management strategies are gaining increasing attention because they are generally considered more acceptable by the traveling public and policy makers. This study presented a detailed analysis and modeling effort aimed at understanding how incentives affected traveler choices by using data collected from a reward-based experiment conducted in 2006 in the Netherlands. The incentive-based scheme analyzed in this study included monetary rewards or credit toward obtaining a smartphone with a view to motivating commuters to change their choice of departure time out of the peak period or to shift their mode of travel. The mixed panel multinomial logit modeling approach adopted in this study was able to isolate the impacts of incentives on behavioral choices while accounting for variations in such impacts across socioeconomic groups that might have been due to unobserved individual preferences and constraints. The model also shed light on the effects of behavioral inertia, in which ...Incentive-based travel demand management strategies are gaining increasing attention because they are generally considered more acceptable by the traveling public and policy makers. This study presented a detailed analysis and modeling effort aimed at understanding how incentives affected traveler choices by using data collected from a reward-based experiment conducted in 2006 in the Netherlands. The incentive-based scheme analyzed in this study included monetary rewards or credit toward obtaining a smartphone with a view to motivating commuters to change their choice of departure time out of the peak period or to shift their mode of travel. The mixed panel multinomial logit modeling approach adopted in this study was able to isolate the impacts of incentives on behavioral choices while accounting for variations in such impacts across socioeconomic groups that might have been due to unobserved individual preferences and constraints. The model also shed light on the effects of behavioral inertia, in which individuals were prone to continue their past behavior even when it was no longer optimal. Finally, the study offered insights on the extent to which behavioral changes persisted after termination of the incentive period. In general, it was found that incentives were effective in changing behavior and overcame inertial effects; however, individuals largely reverted to their original behavior when the rewards were eliminated. This finding suggests that incentives need to be provided for a sustained period to bring about lasting change.