DISCRETE-TIME POINT PROCESSES IN URBAN TRAFFIC QUEUE ESTIMATION

This research was motivated by the belief that it is possible to develop improved algorithms for the computer control of urban traffic. Previous research suggested that the computer software, and especially the filtering and prediction algorithms, is the limiting factor in computerized traffic control. Since the modem approach to filtering and prediction begins with the development of models for the generation of the data and since these models are also useful in the control problem, this paper deals with the modeling of traffic queues and filtering and prediction. It is shown that the data received from vehicle detectors is a DiscreteTime point process. The formation and dispersion of queues at a traffic signal is then modeled by a DiscreteTime time-varying Markov chain which is related to the observation point process. Three such models of increasing complexity are given. Recent dts in the theory of point-process filterinr and prediction are then used to derive the nonlinear minimum error variance fdters/predictors corresponding to these models. It is then shown that these optimal estimators are computationally feasible in a microprocessor. AU three algorithm were tested against the UTCS-1 traffic simulator and, in one case, against an algorithm in current use called ASCOT. Some results of these tats are shown. Ihey indicate gaad performance in every case and better performance than ASCOT in tfie comparable case. Copyright © 1979 by The Institute of Electrical and Electronics Engineers, Inc.