Vehicle Reidentification with the Inductive Loop Signature Technology

Abstract

This study proposed a real-time inductive loop signature based vehicle reidentification approach, RTREID-2M, which improved the previously developed RTREID-2 algorithm on two aspects: (1) Develop a cubic spline data imputation approach to replace the existing linear data imputation approach in order to improve the raw signature data quality; (2) Improve the time window setting for vehicles on High-Occupancy Vehicle (HOV) lanes so that vehicles on HOV lanes traveling with free flow speeds would be considered when generating candidate vehicle sets even during congestion time periods. In addition, a stratified-random sampling method was developed to effectively perform ground-truthing task for evaluating the performance of the proposed RTREID-2M. The evaluation results showed desired performance for vehicle reidentification and travel time estimation under both free-flow and congested flow traffic conditions. The future research will focus on the potential applications and arterial vehicle reidentification utilizing the inductive loop signature technologies.