Anonymous vehicle tracking for real-time traffic surveillance and performance on signalized arterials

Abstract

One of the fundamental requirements for facilitating implementation of any advanced transportation management and information system (ATMIS) is the development of a real-time traffic surveillance system able to produce reliable and accurate traffic performance measures. This study presents a new framework for anonymous vehicle tracking capable of tracing individual vehicles by the vehicle features. The core part of the proposed vehicle tracking method is a vehicle reidentification algorithm for signalized intersections based on inductive vehicle signatures. The new vehicle reidentification system consists of two major components: search space reduction and probabilistic pattern recognition. Not only real-time intersection performance but also intersection origin-destination information can be obtained as the algorithm's basic output. A systematic simulation investigation was conducted of the performance and feasibility of anonymous vehicle tracking on signalized arterials using the Paramics simulation model. Extensive research experience with vehicle reidentification techniques on single roadway segments was the basis for investigating the performance that could be obtained 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 with existing traffic surveillance infrastructure would be an invaluable tool for operating agencies in support of ATMIS strategies for congestion monitoring, adaptive traffic control, system evaluation, and provision of real-time traveler information.