Machine learning-based optimization framework for vehicle reidentification between detectors at signalized intersections

Signalized intersections are equipped with advance and stop-bar detectors that detect vehicles at discrete locations without linking or reidentifying them over the approach area. Accurate tracking and reidentification of vehicles between these detectors could provide valuable driver behavior data, especially during the safety-critical yellow onset periods. However, reidentifying vehicles using non-visual detection data is challenging and not well-explored, with existing analytical models relying on a priori-calibrated parameters. To this end, we propose a machine learning (ML)-based reidentification framework for accurately tracking vehicles over the advance and stop bar loop detectors. The framework comprises two major components: advanced ML and deep learning (DL) models for accurately predicting the travel time between detectors and a novel optimization model that utilizes these predicted travel times and actuation events for reidentifying vehicles. Tests carried out on a major intersection approach in Phoenix, Arizona, showed that the optimization framework based on neural oblivious decision ensemble (NODE) reidentified vehicles even at congested conditions with 94.5% precision and 92.1% recall, outperforming state-of-the-art analytical, conventional ML, and comparable DL models. The low false alarm rate and high recall of this reidentification framework open avenues for obtaining valuable driver behavior data at the yellow onset to analyze stop/go behavior, dilemma zone entry/exit, red light running, and crossing conflicts at signalized intersections.