Anonymous Vehicle Tracking for Real-Time Traffic Performance Measure

A fundamental requirement for the successful implementation of advanced transportation management and information systems (ATMIS) is the development of a real-time traffic surveillance system that can produce accurate and reliable traffic performance measures. My dissertation presents a new framework for anonymous vehicle tracking that is capable of tracking individual vehicles using vehicle features. The core of the proposed method is a vehicle reidentification algorithm for signalized intersections based on inductive vehicle signatures. This has two major components: search space reduction and probabilistic pattern recognition. Both real-time intersection performance and intersection origin-destination (OD) information can be obtained as basic outputs of the algorithm. I developed an evaluation framework for vehicle tracking performance using a microscopic traffic simulation. I conducted a systematic simulation investigation of the performance and feasibility of anonymous vehicle tracking along multiple detector stations using the proposed simulation evaluation framework. The proposed system produces a rich data source for OD estimation. This application is the focus of my dissertation. I also present other useful applications of inductive vehicle signatures. These include the development of a methodology for evaluating traffic safety based on individual vehicle information and the prediction of section travel times. The proposed methodology can also be a valuable tool for operating agencies to support a number of intelligent transportation systems (ITS) strategies including congestion monitoring, adaptive traffic control, system evaluation and provision of real-time traveler information.