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

AbstractThe 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

Information impacts on route choice and learning behavior in a congested network

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 network's 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

Where to park? A behavioural comparison of bus Park and Ride and city centre car park usage in Bath, UK

Integrating car parking facilities with public transport in Park and Ride (P&R) facilities has the potential to shorten car trips, contributing to more sustainable mobility. There is an ongoing debate about the actual effects of P&R on the transport system at the subregional level. A key issue is the relative attractiveness of city centre car parks (CCCP), P&R and public transport. The paper presents the findings of a comparative empirical case-study based on a field survey of CCCP and P&R users conducted in the city of Bath, UK. Spatial and statistical analyses are applied. Radial distance to parking, availability of P&R sites in the direction of travel, gender, age, income and party-size are found to be important factors in a binary logistic regression model, explaining the revealed-preference of parking type. Stated analysis of foregone parking alternatives suggests more use of public transport and walking/cycling would likely occur without first-best parking alternatives. The policy implications and possible planning alternatives to P&R at the urban fringes for achieving greater sustainability goals are also discussed. © 2014 Elsevier Ltd

Short Run Transit Route Planning Decision Support System Using a Deep Learning-Based Weighted Graph

Public transport routing plays a crucial role in transit network design, ensuring a satisfactory level of service for passengers. However, current routing solutions rely on traditional operational research heuristics, which can be time-consuming to implement and lack the ability to provide quick solutions. Here, we propose a novel deep learning-based methodology for a decision support system that enables public transport (PT) planners to identify short-term route improvements rapidly. By seamlessly adjusting specific sections of routes between two stops during specific times of the day, our method effectively reduces times and enhances PT services. Leveraging diverse data sources such as GTFS and smart card data, we extract features and model the transportation network as a directed graph. Using self-supervision, we train a deep learning model for predicting lateness values for road segments. These lateness values are then utilized as edge weights in the transportation graph, enabling efficient path searching. Through evaluating the method on Tel Aviv, we are able to reduce times on more than 9\% of the routes. The improved routes included both intraurban and suburban routes showcasing a fact highlighting the model's versatility. The findings emphasize the potential of our data-driven decision support system to enhance public transport and city logistics, promoting greater efficiency and reliability in PT services

Epilogue: the new frontiers of behavioral research on the interrelationships between ICT, activities, time use and mobility

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. This special issue is a product of the international symposium on “ICT, Activities, Time Use and Travel” that was hosted by Nanjing University from 16 to 18 July 2016. The symposium brought together leading scholars from all over the world to congregate with Chinese scholars and students and to share and discuss the research frontiers at this nexus. It was motivated by a recognition of the changing goals and scope of Information and Communications Technology (ICT) research in conjunction with the development of new ICTs and the emergence of new ICT-enabled behaviors. Consequently, the symposium and later this special issue have drawn together significant scholarly contributions that provide new behavioral insights as well as new theoretical and methodological advances. The symposium culminated in three roundtable panel discussions addressing the following cross-cutting themes: (1) time use while travelling (led by Glenn Lyons); (2) ICT and travel behavior (led by Pat Mokhtarian); and (3) Big Data, activities and urban space (led by Eran Ben-Elia). In this epilogue to the special issue we offer a distillation of these discussions

"If only I had taken the other road...": Regret, risk and reinforced learning in informed route-choice

This paper presents a study of the effect of regret on route choice behavior when both descriptional information and experiential feedback on choice outcomes are provided. The relevance of Regret Theory in travel behavior has been well demonstrated in non-repeated choice environments involving decisions on the basis of descriptional information. The relation between regret and reinforced learning through experiential feedbacks is less understood. Using data obtained from a simple route-choice experiment involving different levels of travel time variability, discrete-choice models accounting for regret aversion effects are estimated. The results suggest that regret aversion is more evident when descriptional information is provided ex-ante compared to a pure learning from experience condition. Yet, the source of regret is related more strongly to experiential feedbacks rather than to the descriptional information itself. Payoff variability is negatively associated with regret. Regret aversion is more observable in choice situations that reveal risk-seeking, and less in the case of risk-aversion. These results are important for predicting the possible behavioral impacts of emerging information and communication technologies and intelligent transportation systems on travelers' behavior. © 2012 Springer Science+Business Media, LLC

Changing commuters' behavior using rewards: A study of rush-hour avoidance

In a 13-week field study conducted in The Netherlands, participants were provided with daily rewards - monetary and in-kind, in order to encourage them to avoid driving during the morning rush-hour. Participants could earn a reward (money or credits to keep a Smartphone handset), by driving to work earlier or later, by switching to another mode or by teleworking. The collected data, complemented with pre and post measurement surveys, were analyzed using longitudinal techniques and mixed logistic regression. The results assert that the reward is the main extrinsic motivation for discouraging rush-hour driving. The monetary reward exhibits diminishing sensitivity, whereas the Smartphone has endowment qualities. Although the reward influences the motivation to avoid the rush-hour, the choice how to change behavior is influenced by additional factors including education, scheduling, habitual behavior, attitudes, and travel information availability. © 2011 Elsevier Ltd. All rights reserved