@conference {
	title = {Estimation of Traffic Platoon Structure from Headway Statistics},
	author = {Baras, John S., Levine, William S., Dorsey, Arthur J.},
	year = {1978},
	month = {12/1978},
	abstract = {This paper deals first with the modeling of urban traffic headway statistics. It is shown that a composite distribution based on the convex combination of a lognormal and a shifted exponential distribution gives a good fit to observed traffic data. This statistical model is then used to generate a model for the formation and passage of "platoons" of vehicles. It is shown that the problem of estimating the time at which a "platoon" passes a detector, as well as the number of vehicles in the disorder "platoon", problem. corresponds An optimal to the estimator point process for the platoon size and passage time, based on detector data, is then derived via known results for the point process disorder problem. It is shown that the computations required by~this estimator can be performed~in a microprocessor. required by this Furthermore, the estimator is tested against the UTCS-1 traffic simulator and performs very well. Parameter sensitivity analysis of the estimator is presented. Finally, the use of these results to improve the filter/predictor described in a companion paper, and vice versa, is explained.},
	address = {San Diego, CA},
	booktitle = {17th IEEE Conference on Decision and Control},
	pages = {pp. 697-703}
}
@conference {
	title = {Discrete Time Point Processes in Urban Traffic Queue Estimation},
	author = {Baras, John S., Levine, William S., Lin, Tahsin L.},
	year = {1978},
	month = {12/1978},
	abstract = {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 modern 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 discrete time point process. The formation and dispersion of queues at a traffic signal is then modeled by a discrete-time time-varying Markov chain which is related to the observation point process. Three such models of increasing complexity are given. Recent results in the theory of point-process filtering and prediction are then used to derive the nonlinear minimum error variance filters/predictors corresponding to these models. It is then shown that these optimal estimators are computationally feasible in a microprocessor. All three algorithms were tested against the UTCS-1 traffic simulator and, in one case, against an algorithm in current use called ASCOT. Some results of these tests are shown. They indicate good performance in every case and better performance than ASCOT in the comparable case.},
	address = {San Diego, CA},
	booktitle = {17th IEEE Conference on Decision and Control},
	pages = {pp. 12-27}
}
