Cité Descartes, Champs sur Marne
77447, Cedex, France.


Ifsttar Batiment Bienvenüe
Ecole des Ponts ParisTech (ENPC)


N. Farhi, N. Bhouri et J-P. Lebacque (Grettia).
Z. Christoforou et F. Leurent (Lvmt).


+331 81 66 87 04

Session prochaine

Date: 8 Mars 2019
Lieu: Ifsttar - Salle B021 - 14 - 20 Boulevard Newton - Marne-la-Vallée, France. Accès .


13h30 Azise Diallo, Guillaume Lozenguez, Arnaud Doniec, René Mandiau, IMT Lille Douai & Université Polytechnique Haut de France,
Titre. Méthodologie de comparaison de simulateurs de trafic. Slides .
14h15 Negin Alisoltani. Ifsttar/Cosys/Licit-Grettia.
Titre. Optimal fleet management for real-time ride-sharing service considering network congestion. Slides .
Résumé. When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ride-sharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Most of the existing approaches focus on the first point, i.e. designing the demand matching while using basic assumptions for the second point, mainly constant travel times. Furthermore, most assume that predicted travel times used for the demand-matching are observed when executing the vehicle schedule, which is usually not the case in practice. In my research two models are defined to deal with dynamic traffic conditions: current mean speed in the network is used over the next 10 minutes to predict travel times when calculating the optimal schedule for the ride-sharing fleet. This fleet is updated every second using a trip-based MFD model as the plant model to represent the traffic dynamics. Some important details are discussed: improvements in the objective functions and also traffic conditions with different values for the number of sharing, the market-rate, and pickup/drop off time window. We find out that the proposed system is really efficient in terms of reducing congestion, especially in peak hours if sufficient sharing happens. Also it can reduce the providers cost while it has small increase in passengers waiting time and travel time.
15h00 Pause Café.
15h15 Cyril Nguyen Van Phu, Ifsttar/Cosys/Grettia.
Titre. Estimation of urban traffic state with probe vehicles. Slides .
Résumé. We present in this paper a method to estimate urban traffic state with communicating vehicles. Vehicles moving on the links of the urban road network form queues at the traffic lights. We assume that a proportion of vehicles are equipped with localization and communication capabilities, and name them probe vehicles. First, we propose a method for the estimation of the vehicles arrival rate on a link, as well as the penetration ratio of probe vehicles. Second, assuming that the turn ratios at each junction are known, we propose an estimation of the queue lengths on a 2-lanes link, by extending a 1-lane existing method. Our method introduces vehicles assignment onto the lanes. Third, based on this approach, we propose optimal control laws for the traffic light and for the assignment of the arriving vehicles onto the lane queues. Finally, simulation experiments are conducted with Veins framework that bi-directionally couples microscopic road traffic and communication simulators. We illustrate and discuss our propositions with the simulation results.
16h00 Mostafa Ameli. Ifsttar/Cosys/Licit-Grettia.
Titre. Simulation-based user equilibrium: improving the fixed point solution methods. Slides .
Résumé. Calculating dynamic network equilibrium is a challenging problem. This study first analyzes the different frameworks for the simulation-based User Equilibrium (UE) in the literature. Most of them are based on solving a fixed-point problem by iterative methods. Two elements have to be determined: the path set between all origin-destination pairs and the optimal path flow distribution. The most advanced solution methods expressed the problem with two-layers, the outer and the inner loop, that tackle each element respectively. The goal of this study is to improve the inner loop, i.e. the path flow calculation. The numerical experiments show that the performance of the different components of the solution algorithm is sensitive to the network size. Finally, the best configurations of the solution algorithms are recommended for all network sizes with a particular focus on the large-scale.