Route choice and traffic equilibrium modeling in multi-modal and activity-based networks

Traveling is an essential part of daily life, whether to attend work, perform social activities, or go shopping among others. We benefit from an increasing range of available transportation services to choose from, which supports economic growth and contributes to our quality of life. Yet the growing demand for travel, combined with urban sprawl and increasing vehicle ownership rates, is also responsible for major local and global externalities, such as degradation of the environment, congestion and noise. In order to mitigate the negative impacts of traveling while weighting benefits to users, transportation planners seek to design policies and improve infrastructure with the best possible outcome for society as a whole. Taking effective actions requires to evaluate the impact of various measures, which necessitates first to understand and predict travel behavior, i.e., how, when and by which route individuals decide to travel.   With this background in mind, this thesis has the objective of developing and applying models to predict flows of persons and/or vehicles in multi-modal transportation networks. It is desirable that such models be data-driven, produce accurate predictions, and be applicable to real networks. In practice, the problem of flow prediction is addressed in two separate steps, and this thesis is concerned with both. The first, route choice analysis, is the problem of identifying the path a traveler would take in a network. This is achieved by estimating from data a parametrized cost function representing travelers' behavior. The second step, namely traffic assignment, aims at distributing all travelers on the network's paths in order to find an equilibrium state, such that no traveler has an interest in changing itinerary. The challenge lies in taking into account the effect of generated congestion, which depends on travelers' route choices while simultaneously impacting their cost of traveling.  This thesis is composed of four articles submitted to international journals and an additional chapter. In all the articles of the thesis, we model an individual's choice of path as a sequence of link choices, using so-called recursive route choice models. This methodology is a state-of-the-art framework which is known to possess the advantage of unbiased parameter estimates and fast assignment procedures, by avoiding to generate choice sets of paths. However, it poses the additional challenge of requiring one to solve embedded dynamic programming problems, and is hence not widely used in the transportation community. This thesis addresses practical and theoretical challenges related to applying this methodological framework to real multi-modal networks. The strength of this thesis consists in large-scale applications which bear computational challenges, as well as some methodological contributions to this modeling framework.   The first article in this thesis is a tutorial on predicting and analyzing path choice behavior using recursive route choice models. The contribution of this article is to familiarize researchers with this methodology, to give intuition on the model properties, to illustrate its advantages through examples, and finally to position this modeling framework within a broader context, by establishing links with recently published work in the inverse optimization and machine learning fields.   Two articles and an additional chapter can be categorized as applications of the methodology to estimate parameters of travel demand models in several large, real, and/or multi-dimensional networks. These applications go beyond previous studies on small physical road networks. First, we estimate recursive models for the route choice of cyclists and we demonstrate some advantages of the recursive models in the context of prediction. We also provide an application to a time-expanded public transportation networks with several modes. Then, we consider a broader travel demand problem, in which decisions regarding daily trips and participation in activities are made jointly. The latter is also modeled with recursive route choice models by considering sequences of activity, destination and mode choices as paths in a so-called supernetwork.   Finally, the subject of the last article in this thesis is traffic assignment. More precisely, we address the problem of computing a traffic equilibrium in networks with strictly limited link capacities, such as public transport networks. This article provides important methodological contributions. We propose a strategic Markovian traffic equilibrium model which assigns flows to networks without exceeding link capacities while realistically modeling how the risk of not being able to access an arc affects route choice behavior.

Keyphrases: Recursive route choice models, Maximum likelihood estimation, Dynamic programming, Multi-modal route choice, Markovian traffic assignment model, Activity-based travel demand