TRAFFIC-RESPONSIVE SIGNAL TIMING FOR SYSTEM-WIDE TRAFFIC CONTROL

Abstract 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 system—human 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.

[1]  Markos Papageorgiou,et al.  Automatic control methods applied to freeway network traffic , 1993, Autom..

[2]  Gordon F. Newell.  THEORY OF HIGHWAY TRAFFIC SIGNALS , 1989 .

[3]  Terutoshi Kaku,et al.  DEVELOPMENT OF A SELF-ORGANIZING TRAFFIC CONTROL SYSTEM USING NEURAL NETWORK MODELS , 1991 .

[4]  J. Spall.  Multivariate stochastic approximation using a simultaneous perturbation gradient approximation , 1992 .

[5]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[6]  A. Rathi.  A CONTROL SCHEME FOR HIGH TRAFFIC DENSITY SECTORS , 1988 .

[7]  Model-free control of nonlinear stochastic systems in discrete time , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[8]  Markos Papageorgiou,et al.  Dynamic modeling, assignment, and route guidance in traffic networks , 1990 .

[9]  D. C. Chin,et al.  Comparative study of stochastic algorithms for system optimization based on gradient approximations , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[10]  E. Rowe.  The Los-Angeles Automated Traffic Surveillance and Control (ATSAC) System , 1991 .

[11]  Keith R. Bisset,et al.  Simulation of traffic flow and control using fuzzy and conventional methods , 1993 .

[12]  Markos Papageorgiou,et al.  A NEURAL NETWORK APPROACH TO FREEWAY NETWORK TRAFFIC CONTROL , 1994 .

[13]  Philip J Tarnoff,et al.  EVALUATION OF OPTIMIZED POLICIES FOR ADAPTIVE CONTROL STRATEGY , 1991 .

[14]  James C. Spall,et al.  A neural network controller for systems with unmodeled dynamics with applications to wastewater treatment , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[15]  Stephen G. Ritchie,et al.  A KNOWLEDGE-BASED DECISION SUPPORT ARCHITECTURE FOR ADVANCED TRAFFIC MANAGEMENT , 1990 .

[16]  J. Spall,et al.  Nonlinear adaptive control using neural networks: estimation with a smoothed form of simultaneous perturbation gradient approximation , 1994, Proceedings of 1994 American Control Conference - ACC '94.