Traffic-responsive signal timing for system-wide traffic control
A long-standing problem in traffic engineering is to optimize the flow of vehicles through a given road network. Improving the timing of the traffic signals at intersections in the network is generally the most powerful and cost-effective means of achieving this goal. However, because of the many complex aspects of a traffic systemhuman behavioral considerations, vehicle flow interactions within the network, weather effects, traffic accidents, long-term (e.g. seasonal) variation, etc.it has been notoriously difficult to determine the optimal signal timing. This is especially the case on a system-wide (multiple intersection) basis. Much of this difficulty has stemmed from the need to build extremely complex models of the traffic dynamics as a component of the control strategy. This paper presents a fundamentally different approach for optimal signal timing that eliminates the need for such complex models. The approach is based on a neural network (or other function approximator) serving as the basis for the control law, with the weight estimation occurring in closed-loop mode via the simultaneous perturbation stochastic approximation (SPSA) algorithm. The neural network function uses current traffic information to solve the current (instantaneous) traffic problem on a system-wide basis through an optimal signal timing strategy. The approach is illustrated by a realistic simulation of a nine-intersection network within the central business district of Manhattan, New York.
neural networks simultaneous perturbation stochastic approximation (spsa) adaptive control transportation systems traffic signal timing