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by Michael B. McIllrath. / Bibliography: leaves 47-52. / Prepared under grant DOT/TSC 849. Originally presented as the author's thesis, (B.S.) in the M.I.T. Dept. of Electrical Engineering and Computer Science, 1976.

Developing real-time traffic diversion strategies is a major issue of Advanced Traffic Management Systems (ATMS), a component of Intelligent Vehicle Highway Systems (IVHS). Traffic diversion utilizes available capacity in the urban network during a congestion-causing event. If an alternative route selected for diversion is not congested at the current time, a certain part of the route may become congested by the time the diverted drivers reach that part of the network. Thus the ability to forecast future traffic variables on each link along various routes in a prompt and accurate fashion may be necessary to ensure the success of a diversion strategy. Forecasting future traffic variables would also be helpful for urban traffic control. In addition, the forecasting model may help assign the vehicles onto the alternate roads, if the information on driver destinations and the routes between a diversion point and the destinations are available. This dissertation is aimed at developing and evaluating two prediction models: link-based model and network-based model. The link-based prediction model has two components. One component is an Auto Regressive Integrated Moving Average (ARIMA) time series model based on the latest (current) traffic data. The other component is the smoothed historical traffic volume (historical average) for that period as obtained from previous days. These two components are combined to represent the dynamic fluctuations in the traffic flow behavior. The combined model is designed to produce the predicted traffic volumes for a look-ahead period of 30 minutes, divided into 6-minute time intervals. The results show that the combined model is promising for light to medium congested traffic conditions. The network-based prediction model combines current traffic, historical average, and upstream traffic. It is presumed that traffic volume on the upstream can be used to predict the downstream traffic in a specific time period. Three prediction models are developed for traffic prediction: a combination of historical average and upstream traffic, a combination of current traffic and upstream traffic, and a combination of all three variables. The three models are evaluated through regression analysis. The third model is found to be the most applicable while the first model was the least. In order to consider current traffic conditions, a heuristic adaptive weighting system is devised based on the relationships between the origin of prediction and the previous periods. The developed models are applied to real freeway data in 15-minute time interval measured by regular induction loop detectors. The prediction models are shown to be capable of producing reliable and accurate forecasts under congested traffic condition. The prediction systems perform better in the 15-minute range than in the ranges of 30-minute or 45-minute. It is noted that the combined models usually produce more consistent forecasts than the historical average. / Ph. D.

This research effort is motivated by the need to quickly obtain origin-destination (0-0) trip information for an urban area, without expending the excessive time and effort usually accompanying survey-based methods. The intent is to utilize this information to facilitate diversion of traffic in real time, in the event of congestion-causing incidents such as accidents. The O-D trip table information is a key to successful diversion planning, where user destinations are considered in developing the plans. Traffic volumes on the links of the road network contain information which can be exploited advantageously to derive the trip patterns. This approach of synthesizing a trip table from link volumes, and perhaps using a prior trip table to guide the derivation, has useful applications in the context of diversion planning. Unlike conventional O-D surveys, it has the potential of yielding results quickly, a requisite for real-time applications. This research work details a new methodology for synthesizing origin-destination (0-0) trip tables. The approach, which is based on a non-proportional assignment, user-equilibrium motivated, linear programming model, is the principal component of this dissertation. The model is designed to determine a traffic equilibrium network flow solution which reproduces the link volume data, if such a solution exists. If such alternate solutions exist, then it is designed to find that which most closely resembles a specified target trip table. However. it recognizes that due to incomplete information, the traffic may not conform to an equilibrium flow pattern, and moreover, there might be inconsistencies in the observed link flow data, or there might be incomplete information. Accordingly, the model permits violations in the equilibrium conditions as well as deviations from the observed link flows, but at suitable incurred penalties in the objective function. A column generation solution technique is presented to optimally solve the problem. The methodology also accommodates a specified prior target trip table, and drives the solution toward a tendency to match this table using user controlled parameters. Implementation strategies are discussed, and an illustration of the proposed method is presented using some sample test networks. The results from the model are discussed vis-a-vis other relevant, available approaches. The quality of the results and the computational effort required are used as a set of criteria in the comparisons. The comparisons of test results demonstrate the superiority of the linear programming model over the other models considered. The model is also applied to a real network of Northern Virginia, where congestion problems present a serious concern. As a result of this experience, several implementation strategies relevant to the application of the model on a real network are presented, and some general conclusions are derived. The potential application of the model in real-time traffic diversion planning for the study area is discussed. Recommendations for further research are also presented. / Ph. D.

The Two-Fluid traffic model is a tool used to measure the quality of service in city traffic networks. The model has been calibrated for the central business district of Roanoke, Virginia and the results have been compared to those from various other cities around the world. In addition, the sensitivity of the Two-Fluid model parameters has been studied for different unit trip lengths used in calibration for the Roanoke CBD. The three-unit trip lengths tested were 1-mile, 2-mile, and 3-mile. Statistical analyses led to the conclusion that the model parameters are not significantly different for different trip lengths. Furthermore, parameter sensitivity was also established for three types of driver behavior, specifically, "aggressive," "normal," and "conservative." The two-fluid trends showed that the two-fluid curve corresponding to the aggressive driver fell below those of the normal and conservative drivers for off-peak traffic conditions but began to approach the normal trend during peak period. On the other hand, the conservative trend continuously maintained higher trip time values for any given stop time, as compared to the other two trends. This implies that aggressive driving behavior can decrease travel times during off-peak conditions, but has little effect during peak periods, where surrounding traffic does not allow excessive speed or lane changing. / M.S.

"CP830 Research Project and Thesis 2". Includes bibliographical references (p. 80-93) Electronic reproduction.[S.l. :s.n.],2003.Electronic data.Mode of access: World Wide Web.System requirements: Adobe Acrobat reader software for PDF files.Access restricted to institutions with a subscription.

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The increasing congestion on the roads in the United States of America has become a topic of much interest among the transportation planners. There is a limit to which there can be building of new highways. Thus, the thrust for better utilization of the existing facilities is gaining ground. This research project is aimed at a solution to problems of increasing travel times due to congestion. A case study of the toll road (Route 44) in the tidewater region of Virginia has been made as an example of the common method of relieving congestion, ridesharing, which is being encouraged. The failure of the proper maintenance of the HOV lanes on the Route 44 leading to the subsequent rescinding of the same has been looked into. The use of DYNAMO programming was made in order to simulate the conditions on the toll road and to get an idea of the various alternatives which could be used to take care of the problem. On the basis of the simulations carried out, and taking into account the views held by the commuters who did not form a part of the ridesharing community, a method in which authorities could give an advantage as far as travel time is concerned, has been proposed. / Master of Science

Macroscopic fundamental diagram is the concept of the highest importance in traffic flow theory used for development of network-wide control strategies. Previous studies showed that so called Arterial Fundamental Diagrams (AFDs) properly depict relationships between major macroscopic traffic variables on urban arterials. Most of these studies used detector’s occupancy as a surrogate measure to represent traffic density. Nevertheless, detector’s occupancy is not very often present in the field data. More frequently, field data from arterial streets provide performance metrics measured at the stop lines of traffic signals, which represent a hybrid of flow and occupancy. When such performance measures are used in lieu of density, the outcomes of the relationships between macroscopic fundamental variables can be confusing. This study investigates appropriateness of using degree of saturation, as a representative surrogate measure of traffic density, obtained from an adaptive traffic control system that utilizes stop-line detectors, for development of AFDs. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection