Nash Game Based Distributed Control Design for Balancing of Traffic Density over Freeway Networks

International audienceIn this paper, we study the problem of optimal balancing of vehicle density in the freeway traffic. The optimization is performed in a distributed manner by utilizing the controllability properties of the freeway network represented by the Cell Transmission Model. By using these properties, we identify the subsystems to be controlled by local ramp meters. The optimization problem is then formulated as a non-cooperative Nash game that is solved by decomposing it into a set of two-players hierarchical and competitive games. The process of optimization employs the communication channels matching the switching structure of system interconnectivity. By defining the internal model for the boundary flows, local optimal control problems are efficiently solved by utilizing the method of Linear Quadratic Regulator. The developed control strategy is tested via numerical simulations in two scenarios for uniformly congested and transient traffic

Optimal Balancing of Freeway Traffic Density: Application to the Grenoble South Ring

International audienceThis paper presents the application of the idea of optimal balancing of traffic density distribution. The idea was previously studied in the papers [1], [2], and here it is implemented to the Grenoble South Ring in the context of the Grenoble Traffic Lab. The traffic on the ring is represented by the Cell Transmission Model that was tuned by using real data and Aimsun micro-simulator. A special attention is paid to the calibration of a flow merging model. A large-scale optimization problem is solved by using decomposition methods and it is implemented by introducing combinatorial procedures. The main difficulties in the implementation as well as the limitations of the designed software are highlighted. Finally, the results of different traffic scenarios on the Grenoble South Ring are presented

A Variable-Length Cell Road Traffic Model: Application to Ring Road Speed Limit Optimization

International audienceIn this paper we propose a variable speed control strategy based on a new Variable-Length cell transmission Model (VLM). The VLM differs from the standard Cell Transmission model in that only a limited number of (variable length) cells are used. Road network is subdivided into several sections which are assumed to be composed of a downstream congested cell followed by a free upstream cell. Both cells have variable lengths and are described by two lumped densities (one congested, the other free). One more state describing the length variation completes the model for each section. The paper also introduces an associated optimal speed control design based on the proposed VLM. The method is illustrated on a closed ring road and is shown to optimize the traveling time per turn

Hybrid stabilizing control on a real mobile robot

To establish empirical verification of a stabilizing controller for nonholonomic systems, the authors implement a hybrid control concept on a 2-DOF mobile robot. Practical issues of velocity control are also addressed through a velocity controller which transforms the mobile robot to a new system with linear and angular velocity inputs. Experiments in the physical meaning of different controller components provide insights which result in significant improvements in controller performanc

Best-effort Highway Traffic Congestion Control via Variable Speed Limits

International audienceThe problem of controlling the congestion front in a single link road section is considered in this paper. For this purpose, we introduce a new variable-length two-cell lumped model composed of; one congested cell, and another in free flow. This model has the advantage of having few states while preserving the vehicle conservation property. This model is used as a basis to design a simple "best-effort" controller that regulates (at its best) the congestion front to some prespecified value. The control law can be implemented using only information about the congestion front position

Towards scalable optimal traffic control

International audienceThis paper deals with scalable control of traffic lights in urban traffic networks. Optimization is done in real time, so as to take into account variable traffic demands.At each cycle of the traffic lights, the optimization concerns times instants where each traffic light starts and ends its green phase: this allows to describe both the duty-cycle and the phase shifts.First, we formulate a global optimization problem, which can be cast as a mixed-integer linear program. To overcome the complexity of this centralized approach, we also propose a decentralized suboptimal algorithm, whose simplicity allows on-line implementation. Simulations show the effectiveness of the proposed strategies

Data fusion algorithms for Density Reconstruction in Road Transportation Networks

International audienceThis paper addresses the problem of density reconstruction in traffic networks with heterogeneous information sources. The network is partitioned in cells in which vehicles flow from their origin to their destination. The state of the network is represented by the densities of vehicles in each cell.Density estimation is of crucial importance in future Intelligent Transportation Systems for monitoring, control, and navigation purposes. However, deploying fixed sensors for this purpose can be very expensive. Therefore, most of fixed sensors networks are rather sparse. On the contrary, recent technologies have enormously increased the availability of relatively inexpensive Floating Car Data. A data fusion algorithm is then proposedto incorporate the two sources of information into a single observer of density of vehicles. The efficiency of the proposed algorithm is shown in a real scenario using data from the Grenoble Traffic Lab fixed sensor network and INRIX Floating Car Data on the Rocade Sud in Grenoble

Source Localization by Gradient Estimation Based on Poisson Integral

International audienceWe consider the problem of localizing the source of a diffusion process. The source is supposed to be isotropic, and several sensors, equipped on a vehicle moving without position information, provide pointwise measures of the quantity being emitted. The solution we propose is based on computing the gradient -- and higher-order derivatives such as the Hessian -- from Poisson integrals: in opposition to other solutions previously proposed, this computation does neither require specific knowledge of the solution of the diffusion process, nor the use of probing signals, but only exploits properties of the PDE describing the diffusion process. The theoretical results are illustrated by simulations

Optimal Sensor Placement in Road Transportation Networks using Virtual Variances

International audienceThis paper addresses the problem of Optimal Sensor Placement in Road Transportation Networks. The per formance of the sensors is measured in terms of estimation error covariance of the Best Linear Unbiased Estimator of cumulative flows in the network over a long period. Sensors are to be placed in such a way that the sum of the error covariance and of a cost penalizing the number of sensors is minimized. The problem, inherently combinatorial, is relaxed using the concept of Virtual Variance. The resulting problem can be cast as a convex problem, whose computational load ismuch lower than the original combinatorial problem. Several variations are discussed, and the algorithm is applied to a regular grid network, for which an explicit comparison with the true optimum is offered, and, using data from the Grenoble Traffic Lab sensor network, to the real-world scenario ofRocade Sud in Grenoble, France