This paper develops and applies a practical method to estimate the benefits of improved reliability of road networks. We present a general methodology to estimate the scheduling costs due to travel time variability for car travel. In contrast to existing practical methods, we explicitly consider the effect of travel time variability on departure time choices. We focus on situations when only mean delays are known, which is typically the case when standard transport models are used. We first show how travel time variability can be predicted from mean delays. We then estimate the scheduling costs of travellers, taking into account their optimal departure time choice given the estimated travel time variability. We illustrate the methodology for air passengers traveling by car to Amsterdam Schiphol Airport. We find that on average planned improvements in network reliability only lead to a small reduction in access costs per trip in absolute terms, mainly because most air passengers drive to the airport outside peak hours, when travel time variability tends to be low. However, in relative terms the reduction in access costs due to the improvements in network reliability is substantial. In our case we find that for every 1 Euro reduction in travel time costs, there is an additional cost reduction of 0.7 Euro due to lower travel time variability, and hence lower scheduling costs. Ignoring the benefits from improved reliability may therefore lead to a severe underestimation of the total benefits of infrastructure improvements.

A demanda por transporte é derivada, e seu resultado está relacionado à necessidade de superar barreiras físicas, envolvendo deslocamentos e, consequentemente, tempo. Como a sociedade moderna é orientada pelo relógio, tempo pode ser considerada uma variável de grande importante na dinâmica diária. Sendo assim, os sistemas de transporte têm papel fundamental para garantir que essa dinâmica seja atendida dentro do esperado. Portanto, os sistemas de transporte devem prover um serviço confiável. Ou seja, a operação dos mesmos deve ser regular, resultando em uma variabilidade mínima no tempo de viagem. Esse trabalho tem por objetivo analisar o comportamento do usuário do transporte aéreo frente à variabilidade do tempo de viagem. Para isso, modelos comportamentais foram estimados a partir de um estudo de caso realizado em Porto Alegre, Rio Grande do Sul, com os viajantes que frequentam o Aeroporto Internacional Salgado Filho. Uma comparação entre diferentes abordagens comportamentais, baseadas na teoria da utilidade esperada, também foi contemplada no estudo, com o intuito de encontrar o melhor modelo representativo do comportamento do público estudado. Os modelos estimados apresentam diferentes formas funcionais não lineares, caracterizando diferentes reações dos tomadores de decisão quando em situações de risco. / The demand for transport is derived, and the result is related to the need to overcome the physical barriers involving displacements and hence time. As modern society is driven by the clock, time can be considered a great important variable in the daily dynamics. Thus, the transport systems has a key role to ensure that this dynamic is met as expected. Therefore, transport systems should provide a reliable service. That is, the operation there of is regular, resulting in a minimal variability in travel time. This work aims to study the air transport user behavior against the variability of travel time. To do this, behavioral models were estimated from a case study conducted in Porto Alegre, Rio Grande do Sul, with travelers attending the Salgado Filho International Airport. A comparison between different behavioral approaches based on expected utility theory, the study was also considered in order to find the best studied model representative of the behavior of the public. The estimated models have different nonlinear functional forms, featuring different reactions of decision-makers when at risk.

A demanda por transporte é derivada, e seu resultado está relacionado à necessidade de superar barreiras físicas, envolvendo deslocamentos e, consequentemente, tempo. Como a sociedade moderna é orientada pelo relógio, tempo pode ser considerada uma variável de grande importante na dinâmica diária. Sendo assim, os sistemas de transporte têm papel fundamental para garantir que essa dinâmica seja atendida dentro do esperado. Portanto, os sistemas de transporte devem prover um serviço confiável. Ou seja, a operação dos mesmos deve ser regular, resultando em uma variabilidade mínima no tempo de viagem. Esse trabalho tem por objetivo analisar o comportamento do usuário do transporte aéreo frente à variabilidade do tempo de viagem. Para isso, modelos comportamentais foram estimados a partir de um estudo de caso realizado em Porto Alegre, Rio Grande do Sul, com os viajantes que frequentam o Aeroporto Internacional Salgado Filho. Uma comparação entre diferentes abordagens comportamentais, baseadas na teoria da utilidade esperada, também foi contemplada no estudo, com o intuito de encontrar o melhor modelo representativo do comportamento do público estudado. Os modelos estimados apresentam diferentes formas funcionais não lineares, caracterizando diferentes reações dos tomadores de decisão quando em situações de risco. / The demand for transport is derived, and the result is related to the need to overcome the physical barriers involving displacements and hence time. As modern society is driven by the clock, time can be considered a great important variable in the daily dynamics. Thus, the transport systems has a key role to ensure that this dynamic is met as expected. Therefore, transport systems should provide a reliable service. That is, the operation there of is regular, resulting in a minimal variability in travel time. This work aims to study the air transport user behavior against the variability of travel time. To do this, behavioral models were estimated from a case study conducted in Porto Alegre, Rio Grande do Sul, with travelers attending the Salgado Filho International Airport. A comparison between different behavioral approaches based on expected utility theory, the study was also considered in order to find the best studied model representative of the behavior of the public. The estimated models have different nonlinear functional forms, featuring different reactions of decision-makers when at risk.

A demanda por transporte é derivada, e seu resultado está relacionado à necessidade de superar barreiras físicas, envolvendo deslocamentos e, consequentemente, tempo. Como a sociedade moderna é orientada pelo relógio, tempo pode ser considerada uma variável de grande importante na dinâmica diária. Sendo assim, os sistemas de transporte têm papel fundamental para garantir que essa dinâmica seja atendida dentro do esperado. Portanto, os sistemas de transporte devem prover um serviço confiável. Ou seja, a operação dos mesmos deve ser regular, resultando em uma variabilidade mínima no tempo de viagem. Esse trabalho tem por objetivo analisar o comportamento do usuário do transporte aéreo frente à variabilidade do tempo de viagem. Para isso, modelos comportamentais foram estimados a partir de um estudo de caso realizado em Porto Alegre, Rio Grande do Sul, com os viajantes que frequentam o Aeroporto Internacional Salgado Filho. Uma comparação entre diferentes abordagens comportamentais, baseadas na teoria da utilidade esperada, também foi contemplada no estudo, com o intuito de encontrar o melhor modelo representativo do comportamento do público estudado. Os modelos estimados apresentam diferentes formas funcionais não lineares, caracterizando diferentes reações dos tomadores de decisão quando em situações de risco. / The demand for transport is derived, and the result is related to the need to overcome the physical barriers involving displacements and hence time. As modern society is driven by the clock, time can be considered a great important variable in the daily dynamics. Thus, the transport systems has a key role to ensure that this dynamic is met as expected. Therefore, transport systems should provide a reliable service. That is, the operation there of is regular, resulting in a minimal variability in travel time. This work aims to study the air transport user behavior against the variability of travel time. To do this, behavioral models were estimated from a case study conducted in Porto Alegre, Rio Grande do Sul, with travelers attending the Salgado Filho International Airport. A comparison between different behavioral approaches based on expected utility theory, the study was also considered in order to find the best studied model representative of the behavior of the public. The estimated models have different nonlinear functional forms, featuring different reactions of decision-makers when at risk.

La représentation des temps de parcours est un enjeu influençant la qualité de l’information transmise aux usagers des réseaux de transport. En particulier, la congestion constitue un inconvénient majeur dont la prise en compte n’est pas toujours maîtrisée au sein des calculateurs d’itinéraires. De même, les évènements comme les réductions de capacité, les perturbations climatiques, ou encore les pics de fréquentation incitent à dépasser la définition statique des temps de parcours. Des travaux antérieurs se sont focalisés sur des temps dynamiques, i.e. dépendants de la date de départ, de manière à affiner le détail de la représentation, et à prendre notamment en compte le caractère périodique des congestions. La considération d’informations en temps réel est aussi une amélioration indéniable, que ce soit lors de la préparation du trajet, ou lorsqu’il s’agit de s’adapter à des perturbations rencontrées en cours de route. Ceci dit, aussi fines qu’elles soient dans les calculateurs disponibles, ces modélisations présentent un inconvénient majeur : elles ne prennent pas en compte toutes les facettes de la variabilité des temps de parcours. Cette variabilité est très importante, en particulier si l’on considère le niveau d’aversion au risque des usagers. En outre, dans un réseau multimodal, les correspondances éventuelles rendent encore plus critique l’incertitude associée aux temps de parcours. En réponse à ces enjeux, les présents travaux de thèse ont ainsi été consacrés à l’étude de temps de parcours stochastiques, i.e. vus comme des variables aléatoires distribuées.Dans une première étape, nous nous intéressons à la modélisation statistique des temps de parcours et à la quantification de leur variabilité. Nous proposons l’utilisation d’un système de lois développé dans le domaine de l’hydrologie, la famille des lois de Halphen. Ces lois présentent les caractéristiques typiques des distributions de temps de parcours, elles vérifient par ailleurs la propriété de fermeture par l’addition sous certaines hypothèses afférentes à leurs paramètres. En exploitant les ratios de moments associés aux définitions de ces lois de probabilité, nous mettons également au point de nouveaux indicateurs de fiabilité, que nous confrontons avec la palette d’indicateurs classiquement utilisés. Cette approche holistique de la variabilité des temps de parcours nous semble ainsi ouvrir de nouvelles perspectives quant au niveau de détail de l’information, notamment à destination des gestionnaires de réseaux.Par la suite, nous étendons le cadre d’analyse aux réseaux, en utilisant les résultats obtenus à l’étape précédente. Différentes lois de probabilité sont ainsi testées dans le cadre de la recherche du plus court chemin stochastique. Cette première étude nous permet de dresser un panorama des chemins identifiés en fonction du choix de modélisation. S’il est montré que le choix du modèle est important, il s’agit surtout d’affirmer que le cadre stochastique est pertinent. Ensuite, nous soulevons la relative inefficacité des algorithmes de recherche du plus court chemin stochastique, ceux-ci nécessitant des temps de calcul incompatibles avec un passage à l’échelle industrielle. Pour pallier cette difficulté, un nouvel algorithme mettant en oeuvre une technique d’accélération tirée du cadre déterministe est développé dans la dernière partie de la thèse. Les résultats obtenus soulignent la pertinence de l’intégration de modèles stochastiques au sein des calculateurs d’itinéraires. / The travel time representation has a major impact on user-oriented routing information. In particular, congestion detection is not perfect in current route planners. Moreover, the travel times cannot be considered as static because of events such as capacity drops, weather disturbances, or demand peaks. Former researches focused on dynamic travel times, i.e. that depend on departure times, in order to improve the representation details, for example concerning the periodicity of congestions. Real-time information is also a significant improvement for users aiming to prepare their travel or aiming to react to on-line events. However these kinds of model still have an important drawback : they do not take into account all the aspects of travel time variability. This dimension is of huge importance, in particular if the user risk aversion is considered. Additionally in a multimodal network, the eventual connections make the travel time uncertainty critical. In this way the current PhD thesis has been dedicated to the study of stochastic travel times, seen as distributed random variables.In a first step, we are interested in the travel time statistical modeling as well as in the travel time variability. In this goal, we propose to use the Halphen family, a probability law system previously developed in hydrology. The Halphen laws show the typical characteristics of travel time distributions, plus they are closed under addition under some parameter hypothesis. By using the distribution moment ratios, we design innovative reliability indexes, that we compare with classical metrics. This holistic approach appears to us as a promising way to produce travel time information, especially for infrastructure managers.Then we extend the analysis to transportation networks, by considering previous results. A set of probability laws is tested during the resolution of the stochastic shortest path problem. This research effort helps us to describe paths according to the different statistical models. We show that the model choice has an impact on the identified paths, and above all, that the stochastic framework is crucial. Furthermore we highlight the inefficiency of algorithms designed for the stochastic shortest path problem. They need long computation times and are consequently incompatible with industrial applications. An accelerated algorithm based on a deterministic state-of-the-art is provided to overcome this problem in the last part of this document. The obtained results let us think that route planners might include travel time stochastic models in a near future.

Recent advances in travel demand modeling have concentrated on adding behavioral realism by focusing on an individual’s activity participation. And, to account for trip-chaining, tour-based methods are largely replacing trip-based methods. Alongside these advances and innovations in dynamic traffic assignment (DTA) techniques, however, time-of-day (TOD) modeling remains an Achilles’ heel. As congestion worsens and operators turn to variable road pricing, sensors are added to networks, cell phones are GPS-enabled, and DTA techniques become practical, accurate time-of-day forecasts become critical. In addition, most models highlight tradeoffs between travel time and cost, while neglecting variations in travel time. Research into stated and revealed choices suggests that travel time variability can be highly consequential. This dissertation introduces a method for imputing travel time variability information as a continuous function of time-of-day, while utilizing an existing method for imputing average travel times (by TOD). The methods employ ordinary least squares (OLS) regression techniques, and rely on reported travel time information from survey data (typically available to researchers), as well as travel time and distance estimates by origin-destination (OD) pair for free-flow and peak-period conditions from network data. This dissertation also develops two models of activity timing that recognize the imputed average travel times and travel time variability. Both models are based in random utility theory and both recognize potential correlations across time-of-day alternatives. In addition, both models are estimated in a Bayesian framework using Gibbs sampling and Metropolis-Hastings (MH) algorithms, and model estimation relies on San Francisco Bay Area data collected in 2000. The first model is the continuous cross-nested logit (CCNL) and represents tour outbound departure time choice in a continuous context (rather than discretizing time) over an entire day. The model is formulated as a generalization of the discrete cross-nested logit (CNL) for continuous choice and represents the first random utility maximization model to incorporate the ability to capture correlations across alternatives in a continuous choice context. The model is then compared to the continuous logit, which represents a generalization of the multinomial logit (MNL) for continuous choice. Empirical results suggest that the CCNL out-performs the continuous logit in terms of predictive accuracy and reasonableness of predictions for three tolling policy simulations. Moreover, while this dissertation focuses on time-of-day modeling, the CCNL could be used in a number of other continuous choice contexts (e.g., location/destination, vehicle usage, trip durations, and profit-maximizing production). The second model is a bivariate multinomial probit (BVMNP) model. While the model relies on discretization of time (into 30-minute intervals), it captures both key dimensions of a tour’s timing (rather than just one, as in this dissertation’s application of the CCNL model), which is important for tour- and activity-based models of travel demand. The BVMNP’s ability to capture correlations across scheduling alternatives is something no existing two-dimensional choice models of tour timing can claim. Both models represent substantial contributions for continuous choice modeling in transportation, business, biology, and various other fields. In addition, the empirical results of the models evaluated here enhance our understanding of individuals’ time-of-day decisions. For instance, average travel time and its variance are estimated to have a negative effect on workers’ utilities, as expected, but are not found to be that practically relevant here, probably because most workers are rather constrained in their activity scheduling and/or work hours. However, correlations are found to be rather strong in both models, particularly for home-to-work journeys, suggesting that if models fail to accommodate such correlations, biased application results may emerge. / text

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