Traffic estimation, sensing, and control in work zone environments

Smart work zones;Traffic estimation;Traffic control;Traffic sensing;Traffic modeling;Passive infrared sensor (PIR) sensors;Convex optimization;Hamilton Jacobi partial differential equation (PDE);Kalman filtering

Li, Yanning

关键词: Smart work zones;    Traffic estimation;    Traffic control;    Traffic sensing;    Traffic modeling;    Passive infrared sensor (PIR) sensors;    Convex optimization;    Hamilton Jacobi partial differential equation (PDE);    Kalman filtering;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/99274/LI-DISSERTATION-2017.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

This dissertation is motivated by practical safety and mobility concerns in freeway work zones. Smart work zone systems are composed of sensors, communication technologies, and data processing algorithms that are used to monitor and disseminate critical information such as congestion and severe slowdowns. Though a large number of smart work zone technologies have been deployed, many systems are still not well understood in terms of the technologies employed and the overall performance of the system. To address this gap, this dissertation develops theoretical, algorithmic, and practical contributions to the improvement of smart work zone systems from the aspects of traffic estimation, sensing, and control.To understand the impact of the sensing technologies and estimation algorithms, several hundred combinations of sensor network configurations and traffic estimation algorithms are assessed in a traffic micro simulator calibrated with data from a work zone in Illinois. The simulations allow the importance of the sensor type and spacing, the accuracy of individual sensors, and the estimation algorithm to be quantified. It is identified that the spacing of sensors is an important factor for improving the traffic estimation accuracy, and significant improvements can be obtained through traffic estimation algorithms relying on nonlinear filtering techniques.When less sophisticated (but more commonly deployed) algorithms are used, dense sensor deployments offer the most improvement in traffic estimation accuracy. Unfortunately, most existing traffic sensor technologies in work zones are expensive, which prohibits dense deployments. Motivated by this result, a low cost and energy efficient work zone traffic sensor is proposed relying on passive infrared sensing. The sensor hardware and software is developed to assess the potential of passive infrared technologies for traffic monitoring. To detect vehicles and estimate vehicle speeds from the passive infrared sensor, unsupervised machine learning algorithms are developed. Field experiments show that the developed sensors are capable of achieving approximately 3% vehicle detection errors and 3 mph root mean square error for the estimated vehicle speeds aggregated in one-minute intervals.Finally, to improve mobility in work zones, the problem of traffic control in work zones is examined. The traffic dynamics on each link in the work zone is modeled using the Hamilton Jacobi Partial Differential Equation (PDE) augmented with flow constraints at the junctions. A model predictive controller is designed which solves the control problem as a single convex program. The numerical scheme used in the algorithm efficiently computes the evolution of traffic dynamics on the network without the discretization of the PDE, and provides a natural framework for a variety of optimal traffic control problems. The effectiveness of the framework is validated in a microsimulation environment.