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A guide to analyzing and predicting traffic. It also covers the various problems encountered when designing traffic signal controls and highways to accommodate the varying volume.
In this thesis, a modified second-order continuum model is used to describe the traffic behaviour along highways. The model is identified and verified using several sets of traffic measurements collected from a major highway in metropolitan Toronto, Canada. A robust nonlinear sliding mode observer is developed to generate estimates of average velocity and density for a segment of a highway within a corridor, given loop detector measurements at the end-points of the segment. The sliding mode approach has several advantages over other estimation techniques such as the Kalman Filtering including proof of estimate convergence and simplified computations. However, the primary advantage is the robustness of the observer with respect to unmodelled dynamics and disturbances. Unmodelled dynamics are associated with the traffic factors whose effects cannot be captured (properly) in the traffic flow models, e.g., road geometry and weather conditions. On the other hand, model disturbances such as unavailable (not measured) traffic flow at a ramp or measurements provided by a faulty detector can also create unpredictable traffic states. Based on the presented traffic model, a systematic design procedure is developed to make the observer robust with respect to the modelling uncertainties and unavailable traffic states. Simulation and experimental results show the effectiveness of the proposed observer in estimating the states of a highway traffic system. Moreover, a new decentralized state feedback linearizing controller for ramp metering using variable structure control is presented. The main aim is to develop a robust controller to locally stabilize freeway traffic despite the presence of disturbances and modelling errors. Simulation results show that the proposed controller provides improved performance in achieving the design objectives over other existing ramp control strategies such as neural network and linear feedback controllers.
Highly regarded for its clarity and depth of coverage, the bestselling Principles of Highway Engineering and Traffic Analysis provides a comprehensive introduction to the highway-related problems civil engineers encounter every day. Emphasizing practical applications and up-to-date methods, this book prepares students for real-world practice while building the essential knowledge base required of a transportation professional. In-depth coverage of highway engineering and traffic analysis, road vehicle performance, traffic flow and highway capacity, pavement design, travel demand, traffic forecasting, and other essential topics equips students with the understanding they need to analyze and solve the problems facing America’s highway system. This new Seventh Edition features a new e-book format that allows for enhanced pedagogy, with instant access to solutions for selected problems. Coverage focuses exclusively on highway transportation to reflect the dominance of U.S. highway travel and the resulting employment opportunities, while the depth and scope of coverage is designed to prepare students for success on standardized civil engineering exams.
This paper presents a microscopic traffic estimation algorithm for smartphones by employing their built-in probes such as GPS and acceleration sensors to increase the accuracy of real-time traffic condition estimation without significantly increasing the smartphones' energy consumption. In this approach, real-time traffic data is collected through the smartphones of participating users traveling on urban roads. A new reporting algorithm is provided on the clients' side to minimize the amount of time the smartphone maintains connection to the server. Based on the data received from each individual smartphone, real-time traffic conditions and the level of service (LOS) are estimated on the server side by applying the Kalman Filtering algorithm and link aggregating speed algorithm. An iOS application is developed to work as a sample client side smartphone node. Simulations of three different traffic scenario are also created to evaluate the performance of the algorithm. Simulation results show that the proposed algorithm requires less energy usage than existing methods without sacrificing the accuracy of real-time traffic estimations.