Anonymous Vehicle Tracking for Real-time Traffic Surveillance and Performance on Signalized Arterials

One of the fundamental requirements to facilitate implementation of any Advanced Transportation Management and Information System (ATMIS) is the development of a real-time traffic surveillance system to produce reliable and accurate traffic performance measures. This study presents a new framework for anonymous vehicle tracking that is capable of tracing individual vehicles by utilizing vehicle features. The core part of the proposed vehicle tracking method is a vehicle reidentification algorithm for signalized intersections based on inductive vehicle signatures, which consists of two major components: search space reduction and probabilistic pattern recognition. Not only real-time intersection performance but also intersection Origin-Destination (OD) information can be obtained as basic outputs of the algorithm. A systematic simulation investigation of the performance and feasibility of anonymous vehicle tracking on signalized arterials using the Paramics simulation model is performed. Extensive research experience with vehicle reidentification techniques on single roadway segments is used to investigate the performance obtainable from tracking individual vehicles across multiple detector stations. The findings of this study serve as a logical and necessary precursor to possible field implementation of vehicle reidentification techniques. The proposed anonymous vehicle tracking methodology utilizing existing traffic surveillance infrastructure would be an invaluable tool for operating agencies in support of various ATMIS strategies including congestion monitoring, adaptive traffic control, system evaluation, and provision of real-time traveler information.