β Scribed by James C. Spall; Daniel C. Chin
No coin nor oath required. For personal study only.
AbstractΓA long-standing problem in trac engineering is to optimize the Β―ow of vehicles through a given road network. Improving the timing of the trac signals at intersections in the network is generally the most powerful and cost-eective means of achieving this goal. However, because of the many complex aspects of a trac systemΓhuman behavioral considerations, vehicle Β―ow interactions within the network, weather eects, trac accidents, long-term (e.g. seasonal) variation, etc.Γit has been notoriously dicult to determine the optimal signal timing. This is especially the case on a system-wide (multiple intersection) basis. Much of this diculty has stemmed from the need to build extremely complex models of the trac dynamics as a component of the control strategy. This paper presents a fundamentally dierent 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 trac information to solve the current (instantaneous) trac 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.
π SIMILAR VOLUMES
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CURRENT TOPICS. 637 ipated for them in the building of safes. Not only are the plates so extremely hard that they cannot be drilled, but they have a remarkable resistance to the flame of an oxyacetylene lamp. It is estimated that to burn a hole three inches in diameter and 3~ inch thick would requir