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

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

Density and flow reconstruction in urban traffic networks using heterogeneous data sources

International audienceIn this paper, we consider the problem of joint reconstruction of flow and density in a urban traffic network using heterogeneous sources of information. The traffic network is modeled within the framework of macroscopic traffic models, where we adopt Lighthill-Whitham-Richards model (LWR) conservation equation characterized by a piecewise linear fundamental diagram. The estimation problem considers two key principles. First, the error minimization between the measured and reconstructed flows and densities, and second the equilibrium state of the network which establishes flow propagation within the network. Both principles are integrated together with the traffic model constraints established by the supply/demand paradigm. Finally the problem is casted as a constrained quadratic optimization with equality constraints in order to shrink the feasible region of estimated variables. Some simulation scenarios based on synthetic data for a manhattan grid network are provided in order to validate the performance of the proposed algorithm