International audience

The probabilistic forecasting method described in this study is devised to leverage spatial and temporal dependency of urban traffic networks, in order to provide predictions accurate at short term and meaningful for a horizon of up to several hours. By design, it can deal with missing data, both for training and running the model. It is able to forecast the state of the entire network in one pass, with an execution time that scales linearly with the size of the network. The method consists in learning a sparse Gaussian copula of traffic variables, compatible with the Gaussian belief propagation algorithm. The model is trained automatically from an historical dataset through an iterative proportional scaling procedure, that is well suited to compatibility constraints induced by Gaussian belief propagation. Results of tests performed on two urban datasets show a very good ability to predict flow variables and reasonably good performances on speed variables. Some understanding of the observed performances is given by a careful analysis of the model, making it to some degree possible to disentangle modeling bias from the intrinsic noise of the traffic phenomena and its measurement process.

Abstract We consider a shared transportation system in an urban environment where human drivers collect vehicles that are no longer being used. Each driver, also called a platoon leader, is in charge of driving collected vehicles as a platoon to bring them back to some given location (e.g., an airport, a railway station). Platoon allocation and route planning for picking up and returning automated vehicles is one of the major issues of shared transportation systems that need to be addressed. In this article, we propose a coalition game approach to compute (1) the allocation of unused vehicles to a minimal number of platoons, (2) the optimized tour of each platoon, and (3) the minimum energy consumed to collect all these vehicles. In this coalition game, the players are the parked vehicles, and the coalitions are the platoons that are formed. This game, where each player joins the coalition that maximizes its payoff, converges to a stable solution. The quality of the solution obtained is evaluated with regard to three optimization criteria and its complexity is measured by the computation time required. Simulation experiments are carried out in various configurations. They show that this approach is very efficient to solve the multiobjective optimization problem considered, since it provides the optimal number of platoons in less than a second for 300 vehicles to be collected, and considerably outperforms other well‐known optimization approaches such as multiobjective particle swarm optimization and nondominated sorting genetic algorithm.

With recent technological advances made in the field of vehicular communications, a plethora of applications has emerged that aim to improve driver safety and traffic fluidity. The platooning application is expected to be a good first step to tame autonomous driving, especially traffic congestion problems in big cities. In this context, it is important to study a platoon of vehicles in its environment, sharing the road and wireless channel with other road users. For such a scenario, efficient simulation tools come in handy for studying vehicular communications in large scale environments. This paper presents a step toward improving iTETRIS simulation platform to simulate platoon communications in large-scale connected vehicles use case. To this end, a restricted communication zone, centered around the leading vehicle of the platoon, and in which vehicles can receive and send packets is introduced to limit the computational cost of communication simulation. The merits of the improved platform in term of time execution efficiency compared to iTETRIS are shown through extensive simulation using real mobility traces.

We introduce a class of Markov models, termed best-effort networks, designed to capture performance indices such as mean transfer times in data networks with best-effort service. We introduce the so-called min bandwidth sharing policy as a conservative approximation to the classical max-min policy. We establish necessary and sufficient ergodicity conditions for best-effort networks under the min policy. We then resort to the mean field technique of statistical physics to analyze network performance deriving fixed point equations for the stationary distribution of large symmetrical best-effort networks. A specific instance of such networks is the star-shaped network which constitutes a plausible model of a network with an overprovisioned backbone. Numerical and analytical study of the equations allows us to state a number of qualitative conclusions on the impact of traffic parameters (link loads) and topology parameters (route lengths) on mean document transfer time.