Connected Vehicle Trajectory–Based Estimation of Cycle-by-Cycle Traffic Volume: A Deep Learning–Based Approach Considering Slowed Vehicles and Spatiotemporal Shockwave Dissipation

Abstract

AbstractMany existing approaches for estimating cycle-by-cycle traffic volume from vehicle trajectories require either a high market penetration rate of sampled trajectories or auxiliary intersection data (e.g., detector counts or calibrated parameters), ...Practical ApplicationsTraffic signal optimization and congestion management rely heavily on accurate, real-time traffic-volume data. Traditionally, transportation agencies collect these data using fixed sensors (e.g., inductive loops), which can be costly ...