Fabrice Marchal

Real Cases Applications of the Fully Dynamic METROPOLIS Tool-Box: an Advocacy for Large-scale Mesoscopic Transportation Systems

This paper presents the pros and cons of the so-called “mesoscopic” approach used in dynamic traffic modeling. After a quick overview of methodologies applied in dynamic models, we discuss the critical issues that mostly determined the shape of past and current research: the time modeling scheme, the level of detail of the approach, the computer implementation strategy and the availability of field data. This paper stresses the importance of modeling departure time choice and information processing. Along this discussion, we describe the features of METROPOLIS, a tool-box designed for the simulation of large-scale transportation systems that proved to be tractable on large-scale, real cases. The system features both within-day and day-to-day dynamics. Lastly, this paper presents simulation results on the wide Parisian region (Ile-de-France).

Efficient Map Matching of Large Global Positioning System Data Sets: Tests on Speed-Monitoring Experiment in Zürich

Speed and location observations from Global Positioning System (GPS) loggers are quickly becoming an important source of data for travel behavior researchers. Postprocessing these data requires identifying the location of the GPS data points on a coded map of the transportation network. The output of the map-matching process is the identification of the routes that were actually taken. This paper presents an innovative map-matching algorithm that relies only on the GPS coordinates and the network topology. Examples are provided on a large data set for the Zurich area. The paper demonstrates the efficiency of the algorithm in regard to accuracy and computational speed.

Modeling Location Choice of Secondary Activities with a Social Network of Cooperative Agents

Activity-based models in transportation science focus on the description of human trips and activities. Modeling the spatial decision for so-called secondary activities is addressed in this paper. Given both home and work locations, where do individuals perform activities such as shopping and leisure? Simulation of these decisions using random utility models requires a full enumeration of possible outcomes. For large data sets, it becomes computationally unfeasible because of the combinatorial complexity. To overcome that limitation, a model is proposed in which agents have limited, accurate information about a small subset of the overall spatial environment. Agents are interconnected by a social network through which they can exchange information. This approach has several advantages compared with the explicit simulation of a standard random utility model: (a) it computes plausible choice sets in reasonable computing times, (b) it can be extended easily to integrate further empirical evidence about travel behavior, and (c) it provides a useful framework to study the propagation of any newly available information. This paper emphasizes the computational efficiency of the approach for real-world examples.

METROPOLIS: Modular System for Dynamic Traffic Simulation

METROPOLIS proposes an interactive environment that simulates automobile traffic in large urban areas. The core of the system is a dynamic simulator that integrates commuters’ departure time and route choice behaviors over large networks: Drivers are assumed to minimize a generalized travel cost function that depends on travel time and schedule delay. This simulator is based on a behavioral driver information process. It allows real-time and off-line simulations. The system also includes a scenario builder and a graphical results viewer. The main ideas underlying METROPOLIS are presented, and preliminary computer simulation experiments are discussed for Geneva, Switzerland.

Dynamic traffic analysis with static data. Guidelines with application to Paris

An innovative approach that uses static origin-destination (O-D) matrices to model travel demand in dynamic urban traffic models is presented. Dynamic traffic assignment models require the use of time-dependent or dynamic O-D matrices whose acquisition and estimation can be costly and whose consistency can prove difficult to maintain, whereas static O-D matrices of overall trip demand have been estimated for various cities during the last decade. In contrast, the synthetic approach describes the user departure time behavior in a separate model that splits the demand specification into two distinct blocks: the departure time behavior and the time-independent trip demand. Pros and cons of this method are presented with an application to the Paris region with the dynamic simulator METROPOLIS. Guidelines for model designers and planners who are considering the shift to dynamic traffic simulation tools are also presented.

Analysis of travel cost components using large-scale dynamic traffic models

The original departure-time-choice model introduced by W. Vickrey (1969) for a single origin-destination (O-D) pair is used as a framework for analysis. The aggregation of the commuter’s travel cost for large-scale dynamic networks extends the results obtained by Vickrey for the basic single O-D pair model. A test-site application and a policy example (extension of staggered hours) are provided for the network of the city of Geneva, Switzerland. The focus is on the morning peak hour of private transportation networks.