MODELING AND OPTIMIZING ROUTING DECISIONS FOR TRAVELERS AND ON-DEMAND SERVICE PROVIDERS

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

Road network performance: A review on relevant concepts

La representation des temps de parcours est un enjeu influencant la qualite de l’information transmise aux usagers des reseaux de transport. En particulier, la congestion constitue un inconvenient majeur dont la prise en compte n’est pas toujours maitrisee au sein des calculateurs d’itineraires. De meme, les evenements comme les reductions de capacite, les perturbations climatiques, ou encore les pics de frequentation incitent a depasser la definition statique des temps de parcours. Des travaux anterieurs se sont focalises sur des temps dynamiques, i.e. dependants de la date de depart, de maniere a affiner le detail de la representation, et a prendre notamment en compte le caractere periodique des congestions. La consideration d’informations en temps reel est aussi une amelioration indeniable, que ce soit lors de la preparation du trajet, ou lorsqu’il s’agit de s’adapter a des perturbations rencontrees en cours de route. Ceci dit, aussi fines qu’elles soient dans les calculateurs disponibles, ces modelisations presentent un inconvenient majeur : elles ne prennent pas en compte toutes les facettes de la variabilite des temps de parcours. Cette variabilite est tres importante, en particulier si l’on considere le niveau d’aversion au risque des usagers. En outre, dans un reseau multimodal, les correspondances eventuelles rendent encore plus critique l’incertitude associee aux temps de parcours. En reponse a ces enjeux, les presents travaux de these ont ainsi ete consacres a l’etude de temps de parcours stochastiques, i.e. vus comme des variables aleatoires distribuees.Dans une premiere etape, nous nous interessons a la modelisation statistique des temps de parcours et a la quantification de leur variabilite. Nous proposons l’utilisation d’un systeme de lois developpe dans le domaine de l’hydrologie, la famille des lois de Halphen. Ces lois presentent les caracteristiques typiques des distributions de temps de parcours, elles verifient par ailleurs la propriete de fermeture par l’addition sous certaines hypotheses afferentes a leurs parametres. En exploitant les ratios de moments associes aux definitions de ces lois de probabilite, nous mettons egalement au point de nouveaux indicateurs de fiabilite, que nous confrontons avec la palette d’indicateurs classiquement utilises. Cette approche holistique de la variabilite des temps de parcours nous semble ainsi ouvrir de nouvelles perspectives quant au niveau de detail de l’information, notamment a destination des gestionnaires de reseaux.Par la suite, nous etendons le cadre d’analyse aux reseaux, en utilisant les resultats obtenus a l’etape precedente. Differentes lois de probabilite sont ainsi testees dans le cadre de la recherche du plus court chemin stochastique. Cette premiere etude nous permet de dresser un panorama des chemins identifies en fonction du choix de modelisation. S’il est montre que le choix du modele est important, il s’agit surtout d’affirmer que le cadre stochastique est pertinent. Ensuite, nous soulevons la relative inefficacite des algorithmes de recherche du plus court chemin stochastique, ceux-ci necessitant des temps de calcul incompatibles avec un passage a l’echelle industrielle. Pour pallier cette difficulte, un nouvel algorithme mettant en oeuvre une technique d’acceleration tiree du cadre deterministe est developpe dans la derniere partie de la these. Les resultats obtenus soulignent la pertinence de l’integration de modeles stochastiques au sein des calculateurs d’itineraires.

Modélisation de la variabilité des temps de parcours et son intégration dans des algorithmes de recherche du plus court chemin stochastique

The unavoidable travel time variability in transportation networks, resulted from the widespread supply side and demand side uncertainties, makes travel time reliability (TTR) be a common and core interest of all the stakeholders in transportation systems, including planners, travelers, service providers, and managers. This common and core interest stimulates extensive studies on modeling TTR. Researchers have developed a range of theories and models of TTR, many of which have been incorporated into transportation models, policies, and project appraisals. Adopting the network perspective, this paper aims to provide an integrated framework for reviewing the methodological developments of modeling TTR in transportation networks, including its characterization, evaluation and valuation, and traffic assignment. Specifically, the TTR characterization provides a whole picture of travel time distribution in transportation networks. TTR evaluation and TTR valuation (known as the value of reliability, VOR) simply and intuitively interpret abstract characterized TTR to be well understood by different stakeholders of transportation systems. TTR-based traffic assignment investigates the effects of TTR on the individual users travel behavior and consequently the collective network flow pattern. As the above three topics are mainly separately studied in different disciplines and research areas, the integrated framework allows us to better understand their relationships and may contribute to developing possible combinations of TTR modeling philosophy. Also, the network perspective enables to focus on common challenges of modeling TTR, especially the uncertainty propagation from the uncertainty sources to the TTR at spatial levels including link, route, and the entire network. Some directions for future research are discussed in the era of new data environment, applications, and emerging technologies. 

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Travel time reliability in transportation networks: A review of methodological developments

Public transport route choice is an open problem studied for many years and it is well regarded that many factors influence the choice of stages making up a public transport trip. In addition to factors such travel time, fare, service frequency, number of transfers and transfer waiting time, the focus of research shifted more and more to dynamic factors such as crowdedness and travel time variability. This paper describes a method to simulate public transport route choice for a whole population in a city or even a country taking into account all factors mentioned above using the MATSim framework, a multi-agent and activity-based transport simulator. A new public transport router is proposed using a time dependent shortest path algorithm in a graph with links representing in-vehicle travel, waiting and walking (from activity locations to public transport facilities and between public transport facilities). The cost of the path can be specified to depend on the fares, travel times, occupancy levels, waiting times and number of transfers. In addition, the agent-based nature of the simulation model allows to define agent-specific parameters for each attribute to account for preference heterogeneity within the agent population. Simulated dynamic effects such as bus bunching and overcrowded services lead to additional waiting and travel times, compared with the public transport schedule. Within the MATSim evolutionary algorithm, this information is saved after each simulation of the same day, and used to re-route agents. The router is implemented and tested for Singapore, whose modeled public transport system features bus and subway. As in most major cities, the model shows that the stability and schedule reliability of bus services can at times only hardly be maintained, while crowdedness at the peak hours is an issue for both bus and mass rapid transit and light rapid transit. We illustrate how the new approach allows agents to adapt to these dynamic complexities. The new approach, along with the original scheduled-based router of MATSim are compared for computation time, as well as for agent learning rate, i.e. the time needed to reach a particular solution state. With a better capacity to model observed public transport route choice, more than 10% of improvement in computation time per simulation iteration, and needing much smaller number of iterations to reach user equilibrium, the proposed alternative results a Pareto-dominant improvement.

New dynamic events-based public transport router for agent-based simulations

Reliability is an important factor in route choice analysis and is a key performance indicator for transport systems. However, the current parameters used to measure travel time variability may be not sufficient to fully represent reliability. Better understanding of the distributions of travel times is needed for the development of improved metrics for reliability. A comprehensive data analysis involving the assessment of longitudinal travel time data for two urban arterial road corridors in Adelaide, Australia demonstrates that the observed distributions are more complex than previously assumed. The data sets demonstrate strong positive skew, very long upper tails, and sometimes bimodality. This paper proposes the use of alternative statistical distributions for travel time variability, with the Burr Type XII distribution emerging as an appropriate model for both links and routes.

S Somenahalli

Papers

Distributions of travel time variability on urban roads: a longitudinal study