@conference {
	title = {Overview of Some Recent Results on Traffic Control},
	author = {Levine, William S., Baras, John S.},
	year = {1981},
	month = {06/1981},
	abstract = {This paper presents an overview of the authors{\textquoteright} recent research on urban traffic control. Since the technical details are very complicated and well reported elsewhere the emphasis is on placing these results in a traffic context. A fairly complete solution to the optimal control of an isolated intersection is outlined. The extension of these results to networks is sketched. Finally, some interesting areas for further research are given.},
	address = {Henniker, New Hampshire},
	booktitle = {Engineering Foundation Conference on Issues in Control of Urban Traffic Systems},
	pages = {pp. 43-49}
}
@conference {
	title = {Some Results on Computer Control of Urban Traffic},
	author = {Baras, John S., Levine, William S.},
	year = {1980},
	month = {06/1980},
	abstract = {The problem of controlling vehicle traffic on an urban street network is a particular example of a large scale systems problem,~In a reasonably typical~network there will be one thousand or more traffic signals, there may be several hundred sensors (vehicle detectors) and the network is typically distributed over~several hundred square miles. It is now quite common to control these systems primarily from a central computer. The traffic control problem is modelled by a kind of "store and forward" network in which vehicles are {\textquoteright}stored{\textquoteright} in queues at the signals and "forwarded{\textquoteright} in platoons. The data from detectors is modelled as a point process that is statistically related~to the queues and platoons.~It would be very tempting to design optimal feedback control algorithms for these~systems. However, the size of the problems makes this impossible. Instead sub-optimal controls are developed. These sub-optimal controls use the minimum error variance estimator of the queues at each intersection as determined from the above model to improve the control provided by several intuitively derived sub-optimal~controls. The sub-optimal controls have been tested using a good simulation of urban traffic. The sub-optimal controls give substantially better performance than benchmark open loop controls. Conditions under which the network can be decomposed~than~into subnetworks without significant degradation in performance are also given.~},
	address = {Toulouse, France},
	booktitle = {IFAC Symposium on Large Scale Systems: Theory and Applications},
	pages = {pp. 449-458}
}
@conference {
	title = {Algorithms for the Computer Control of Urban Traffic},
	author = {Baras, John S., Levine, William S.},
	year = {1979},
	month = {08/1979},
	abstract = {Recent developments in computer technology have made substantial computational~capability, in the form of mini- and micro-computers, available at modest prices.~This, coupled with the available traffic sensors, controllable traffic signals and~communications, makes the computer control of traffic feasible in many urban areas~today. Many such systems are either operating or are being planned.~

Software for today{\textquoteright}s hardware environment can be oriented toward several, or~many, small machines each of which controls a small local area. Alternatively,the software can do more pre-processing of data in a small machine located near~the detector thereby reducing communication costs.~

This paper describes a set of filtering, prediction and control algorithms~that are primarily intended for traffic responsive control of networks. A way~to partition the network algorithms among a collection of microprocessors is~proposed. Because of the way in which the control computations can be~partitioned, the algorithms will also work well for isolated intersections.},
	address = {University of California, Berkeley},
	booktitle = {International Symposium on Traffic Control Systems}
}
@conference {
	title = {On the Point Process Disorder Problem and Applications to Urban Traffic Estimation},
	author = {Dorsey, Arthur J., Baras, John S., Levine, William S.},
	year = {1979},
	month = {03/1979},
	abstract = {We consider here the point process disorder problem in its generality and we develop the maximum likelihood and MAP estimates of the disorder time T. Applications of the results to the platoon determination problem in urban traffic show that both estimators outperform previous solutions to the problem.},
	address = {Baltimore, MD},
	booktitle = {1979 Conference on Information Sciences and Systems},
	pages = { pp. 37-42},
	organization = {Johns Hopkins University}
}
@conference {
	title = {Traffic Estimation Based On Point Process Models},
	author = {Baras, John S., Levine, William S., Dorsey, Arthur J., Lin, Tahsin L.},
	year = {1979},
	month = {02/1979},
	abstract = {Two families of models for the relation between detector data and the flow of traffic on urban streets are developed. The first describes the relation between the queues at traffic signal and data from nearby detectors. The second model~headways statistics for urban traffic thereby relating detector data to the passage of platoons of vehicles. Recent results in the theory of point processes then give the non-linear minimum error variance filters/predictors corresponding to these models. It is shown that these optimal~estimators are computationally feasible in a microprocessor. The algorithm also performs very well when tested using data derived from UTCS-I traffic simulation.},
	address = {Asilomar conference grounds, Pacific Grove,  CA},
	booktitle = {Engineering Foundation Conference on Issues in Control of Urban Traffic Systems},
	pages = {pp. 266-285}
}
@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}
}
@conference {
	title = {Estimation of Traffic Flow Parameters in Urban Traffic Networks},
	author = {Baras, John S., Levine, William S.},
	year = {1977},
	month = {06/1977},
	abstract = {The basic problem addressed in this paper is the development of better software for the computer control of urban traffic. Previous research indicates that the computer software, and especially the filtering and prediction algorithm, is the limiting factor in computerized traffic control. Recently developed techniques for the filtering, prediction~and control of point processes, the type of statistics that occur in traffic flow, are used to develop filtering and prediction algorithms to facilitate the development of traffic responsive urban traffic control systems.},
	address = {San Francisco, CA},
	booktitle = {The 1977 JACC},
	pages = {pp. 428-434}
}
@conference {
	title = {Filtering Techniques For Urban Traffic Data},
	author = {Baras, John S., Levine, William S.},
	year = {1976},
	month = {12/1976},
	abstract = {There is evidence that the algorithms for estimating traffic flows from sensor data need to be improved before computer controlled traffic responsive urban traffic control systems can reach their full potential effectiveness . A large part of the problem appears to be that the data - from traffic sensors is, in the statistical jargon, a marked point process. It is only very recently that the theoretical techniques for estimation based on point process data have reached the sophistication needed for traffic problems. In this paper, these techniques are used to derive several recursive algorithms for filtering traffic sensor data and predicting urban traffic flows. These filters and predictors are then evaluated and compared using simulated traffic data .},
	address = {Clearwater, Florida},
	booktitle = {1976 Conference on Decision and Control (CDC)},
	pages = {pp. 1297-1298}
}
@conference {
	title = {Filtering and Prediction Algorithms for Urban Traffic Control Systems},
	author = {Baras, John S., Levine, William S.},
	year = {1976},
	month = {07/1976},
	abstract = {There is evidence that the algorithms for estimating traffic flows from~sensor data need to be improved before computer controlled traffic responsive~urban traffic control systems can reach their full potential effectiveness. A~large part of the problem appears to be that the data from traffic sensors is,in the statistical jargon, a marked point process. It is only very recently that the theoretical techniques for estimation based on point process data have reached the sophistication needed for traffic problems. Thus, this paper~describes and illustrates the way in which these techniques are being applied~to the design of algorithms for filtering and prediction of urban traffic flows.},
	address = {Los Angeles, California},
	booktitle = {4th Intersociety Conference on Transportation},
	pages = {pp. 1-17}
}
@article {
	title = {Estimation of Traffic Platoon Structure from Headway Statistics},
	author = {Baras, John S., Dorsey, Arthur J., Levine, William S.},
	year = {1979},
	month = {08/1979},
	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 "platoon" corresponds to the point process disorder problem. An optimal estimator 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 for this estimator can be performed in a microprocessor . 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.},
	pages = {pp. 553-559},
	volume = {AC-24},
	number = {No. 4},
	journal = {IEEE Transactions on Automatic Control.}
}
@article {
	title = {Discrete Time Point Processes in Urban Traffic Queue Estimation},
	author = {Baras, John S., Levine, William S., Lin, Tahsin L.},
	year = {1979},
	month = {02/1979},
	abstract = {Thk 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 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 tests are shown. They indicate good performance in every case and better performance than ASCOT in the comparable case.},
	pages = {pp. 12-27},
	volume = {AC-24},
	number = {No. 1},
	journal = {IEEE Transactions on Automatic Control.}
}
