Modeling and Optimizing Routing Decisions for Travelers and On-demand Service Providers

This thesis investigates the dynamic routing decisions for individual travelers and on-demand service providers (e.g., regular taxis, Uber, Lyft, etc). For individual travelers, this thesis models and predicts route choice at two time-scales: the day-to-day and within-day. For day-to-day route choice, methodological development and empirical evidences are presented to understand the roles of learning, inertia and real-time travel information on route choices in a highly disrupted network based on data from a laboratory competitive route choice game. The learning of routing policies instead of simple paths is modeled when real-time travel information is available, where a routing policy is defined as a contingency plan that maps realized traffic conditions to path choices. Using data from a competitive laboratory experiment, prediction performance is then measured in terms of both one-step and full trajectory predictions. For within day route choice, a recursive logit model is formulated in a stochastic time-dependent (STD) network without sampling any choice sets. A decomposition algorithm is then proposed so that the model can be estimated in reasonable time. Estimation and prediction results of the proposed model are presented using a data set collected from a subnetwork of Stockholm, Sweden. Taxis and ride-sourcing vehicles play an important role in providing on-demand mobility in an urban transportation system. Unlike individual travelers, they do not have a clear destination when there\u27s no passenger on board. The optimal routing of a vacant taxi is formulated as a Markov Decision Process (MDP) problem to maximize long-term profit over the full working period. Two approaches are proposed to solve the problem. One is the model-based approach where a model of the state transitions of the environment is obtained from queuing-theory based passenger arrival and competing taxi distribution processes. An enhanced value iteration for solving the MDP problem is then proposed making use of efficient matrix operations. The other is the model-free Reinforcement Learning (RL) approach, which learns the best policy directly from observed trajectory data. Both approaches are implemented and tested in a mega city transportation network with reasonable running time, and a systematic comparison of the two approaches is also provided

The assignment of trips to a road network for the analysis of equitable transport

RÉSUMÉ Depuis plusieurs années, le transport durable et le rôle de l’automobile dans les grands centres urbains suscitent beaucoup d’intérêt. La congestion routière, la pollution, les gaz à effet de serre, la consommation énergétique et l’étalement urbain ont motivé le développement de nombreuses stratégies qui visent à modifier le comportement des voyageurs, surtout à l’intérieur des villes. En même temps, les discussions concernant la meilleure façon de financer les grandes infrastructures en transport cherchent à trouver un compromis entre les principes d’efficacité et les principes d’équité. Cette thèse a comme objectif de contribuer, d’une manière modeste, à ces discussions en clarifiant les enjeux entourant le « problème » du transport routier. Le projet de recherche décrit dans cette thèse est fondé sur deux concepts déjà bien documentés : une analyse des systèmes de transport urbain totalement désagrégée basée sur l’information, de même que sur l’équité géopolitique en transport. Dans le cas actuel, l’analyse désagrégée est appliquée à un sous-échantillon de l’enquête origine-destination de la grande région de Montréal effectué en 2003. Le concept d’équité géopolitique est appliqué à l’étude des 15 ponts qui relient la ville de Montréal au réseau routier nord-américain. Ces grandes infrastructures jouent un rôle essentiel de redistribution parmi les nombreuses municipalités qui font partie de la grande région métropolitaine. Outre la redistribution des personnes et des marchandises, les ponts permettent également la redistribution des coûts externes du transport, spécifiquement la congestion routière, la pollution et le bruit. L’enquête origine-destination a demandé à chaque conducteur d’indiquer lequel des ponts ils ont utilisé pour compléter leurs déplacements. Dans cette recherche, les réponses ont été soigneusement examinées afin de construire un sous-échantillon valide des déclarations de pont pour la période de pointe du matin d’une journée moyenne de la semaine. Le sous-échantillon validé était composé de 8 583 observations. En même temps, un modèle détaillé du réseau routier, comprenant plus de 100,000 liens et 70,000 nœuds, a été construit. Les liens étaient classifiés par fonction et par juridiction (municipale, provinciale ou fédérale). Puisque ces simulations étaient basées sur l’approche totalement désagrégée, elles ne comportent aucun système de zone, aucun centroïde et aucune matrice origine-destination. ----------ABSTRACT The past several years have witnessed a growing interest in sustainable urban transportation and a re-evaluation of the role of the automobile in large urban areas. Traffic congestion, air pollution, greenhouse gas emissions, energy consumption and urban sprawl are all topics that have stimulated the development of various strategies that aim to change the way people travel, especially within cities. At the same time, the issues surrounding the best way to finance major transportation infrastructure are framed in a debate about efficiency versus equity. This thesis proposes to contribute to these discussions by clarifying, to a modest degree, the “problem” of urban automobile travel. The research described in this dissertation is founded on two already-documented concepts: the totally disaggregate information-based approach to urban transportation simulation and geopolitical equity. Following the precepts of the former, this research uses data contained within the 2003 Montreal travel survey. With regard to the latter, the research subjects are the 15 bridges that connect the island-city of Montreal to the mainland. These infrastructure elements play a vital role in the redistribution, among the dozens of municipalities within the urban region, of people and goods and of the external costs of travel, particularly traffic congestion, air pollution and noise. The travel survey asked automobile drivers to indicate which major bridge they used over the course of their trip. Their responses were meticulously examined to construct a valid sub-sample of declarations describing bridge usage patterns during the a.m. peak period of a typical average weekday. The final sub-sample contained 8,583 observations. Meanwhile, a model road network of the Greater Montreal Area was constructed. This complete network contained over 100,000 links and 70,000 nodes. The links are categorized by functional class and by jurisdiction (municipal, provincial or federal). Since all the simulations are based on the totally disaggregate approach, there is no zone system, no centroids and no origin-destination matrix

Toll competition in highway transportation networks

Within a highway transportation network, the social welfare implications of two different groups of agents setting tolls in competition for revenues are studied. The first group comprises private sector toll road operators aiming to maximise revenues. The second group comprises local governments or jurisdictions who may engage in tax exporting. Extending insights from the public economics literature, jurisdictions tax export because when setting tolls to maximise welfare for their electorate, they simultaneously benefit from revenues from extra-jurisdictional users. Hence the tolls levied by both groups will be higher than those intended solely to internalise congestion, which then results in welfare losses. Therefore the overarching question investigated is the extent of welfare losses stemming from such competition for toll revenues. While these groups of agents are separately studied, the interactions between agents in each group in competition can be modelled within the common framework of Equilibrium Problems with Equilibrium Constraints. Several solution algorithms, adapting methodologies from microeconomics as well as evolutionary computation, are proposed to identify Nash Equilibrium toll levels. These are demonstrated on realistic transportation networks. As an alternative paradigm to competition, the possibilities for co-operation between agents in each group are also explored. In the case of toll road operators, the welfare consequences of competition could be positive or adverse depending on the interrelationships between the toll roads in competition. The results therefore generalise those previously obtained to a more realistic setting investigated here. In the case of competition between jurisdictions, it is shown that the fiscal externality of tax exporting resulting from their toll setting decisions can substantially reduce the welfare gains from internalising congestion. The ability of regulation, co-operation and bilateral bargaining to reduce the welfare losses are assessed. The research thus contributes to informing debates regarding the appropriate level of institutional governance for toll pricing policies