Michel Bierlaire

Discrete choice methods and their applications to short-term travel decisions

Modeling travel behavior is a key aspect of demand analysis, where aggregate demand is the accumulation of individuals’ decisions. In this chapter, we focus on “short-term” travel decisions. The most important short-term travel decisions include choice of destination for a non-work trip, choice of travel mode, choice of departure time and choice of route. It is important to note that short-term decisions are conditional on long-term travel and mobility decisions such as car ownership and residential and work locations.

Hybrid Choice Models: Progress and Challenges

We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.

Investigating Consumers' Tendency to Combine Multiple Shopping Purposes and Destinations

Due to the increasing time pressure that they face, many consumers are becoming more concerned about the efficiency of their shopping patterns. Retailers have recognize this trend, have improved shopping convenience by offering greater variety in product categories and making it easier for consumers to combine visits to multiple stores. However, little is known about how consumers improve the efficiency of their shopping trips, or how changes in retail supply affect the way in which consumers combine multiple purposes and destinations. Building on previous work in consumer shopping trip modeling and conjoint design theory, this paper introduces a choice-based conjoint approach to studying and modeling this phenomenon. The approach is illustrated in a case study which investigated the tendency of Dutch shoppers to combine grocery, drugstore and clothing purchases across multiple shopping destinations. It was observed that the tendency of consumers to combine purchases differed from category to category and also depended on category availability. In general, consumers combined considerably less purchases than could be expected if their shopping trip planning were based purely on travel cost minimization.

Network state estimation and prediction for real-time traffic management

Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) have the potential to contribute to the solution of the traffic congestion problem. DynaMIT is a real-time system that can be used to generate guidance for travelers. The main principle on which DynaMIT is based is that information should be consistent, and user optimal. Consistency implies that the traffic conditions experienced by the travelers are consistent with the condition assumed in generating the guidance. To generate consistent user optimal information, DynaMIT performs two main functions: state estimation and prediction. A demand simulator and a supply simulator interact to perform these tasks. A case study demonstrates the value of the system.

DEVELOPMENT OF A ROUTE GUIDANCE GENERATION SYSTEM FOR REAL-TIME APPLICATION

Abstract This paper describes the route guidance generation component of DynaMIT, a dynamic traffic assignment (DTA) system intended for deployment in a traffic center and capable of generating real-time prediction-based guidance information. After providing a general overview of the system, the paper discusses the principal theoretical and algorithmic issues which influenced the development of its guidance generation component.

Real-time simulation of traffic demand-supply interactions within DynaMIT

DynaMIT is a simulation-based real-time system designed to estimate the current state of a transportation network, predict future traffic conditions, and provide consistent and unbiased information to travelers. To perform these tasks, efficient simulators have been designed to explicitly capture the interactions between transportation demand and supply. The demand reflects both the OD flow patterns and the combination of all the individual decisions of travelers while the supply reflects the transportation network in terms of infrastructure, traffic flow and traffic control. This paper describes the design and specification of these simulators, and discusses their interactions.

An analytic finite capacity queueing network model capturing the propagation of congestion and blocking

Analytic queueing network models often assume infinite capacity queues due to the difficulty of grasping the between-queue correlation. This correlation can help to explain the propagation of congestion. We present an analytic queueing network model which preserves the finite capacity of the queues and uses structural parameters to grasp the between-queue correlation. Unlike pre-existing models it maintains the network topology and the queue capacities exogenous. Additionally, congestion is directly modeled via a novel formulation of the state space of the queues which explicitly captures the blocking phase. The model can therefore describe the sources and effects of congestion. The model is formulated for networks with an arbitrary topology, multiple server queues and blocking-after-service. It is validated by comparison with both pre-existing methods and simulation results. It is then applied to study patient flow in a network of units of the Geneva University Hospital. The model has allowed us to identify three main sources of bed blocking and to quantify their impact upon the different hospital units.

Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences

In this paper we address the problem of detection and tracking of pedestrians in complex scenarios. The inclusion of prior knowledge is more and more crucial in scene analysis to guarantee flexibility and robustness, necessary to have reliability in complex scenes. We aim to combine image processing methods with behavioral models of pedestrian dynamics, calibrated on real data. We introduce Discrete Choice Models (DCM) for pedestrian behavior and we discuss their integration in a detection and tracking context. The obtained results show how it is possible to combine both methodologies to improve the performances of such systems in complex sequences.

Discrete Choice Models with Applications to Departure Time and Route Choice

Discrete choice methods are constantly evolving to accommodate the requirements of specific applications. This is an exciting field of research, where a deep understanding of the underlying theoretical assumptions is necessary both to apply the models and develop new ones. In this Chapter, we have summarized the fundamental aspects of discrete choice theory, and we have introduced recent model developments, illustrating their richness. A discussion on route choice and departure time choice applications have shown how specific aspects of real applications must be addressed.

A Practical Test for the Choice of Mixing Distribution in Discrete Choice Models

Reference TRANSP-OR-CONF-2006-021 URL: http://transp-or.epfl.ch/documents/proceedings/FosgBier06.pdf URL: http://www.strc.ch/pdf_2006/Fosgerau_Bierlaire_STRC_2006.pdf Record created on 2008-02-15, modified on 2017-10-16