Queue Length Estimation of Intersection Traffic Flow Undera Connected Vehicles Environment

摘要

Traffic state identification is of great importance for the analysis and optimization of traffic management. Queue length of the traffic flow (QLTF) at intersections is a key parameter for signal timing. However, QLTF is not easy to obtain for traditional traffic detection systems. The emerging connected vehicles technology, which can obtain real-time traffic information to facilitate traffic state identification and traffic system optimization, is attracting more and more attention. This paper puts forward a method to estimate the end of vehicle queue position for varied Market Penetration Rate (MPR) with connected vehicle technology. Using VISSIM traffic simulation software and the Matlab COM mathematical software package, experiments were carried out and evaluated in different MPRs (50%, 70%, 80%) and traffic flow volumes (1600, 2000, 2400 pcu/h). The results demonstrate that this proposed algorithm can obtain the real-time queue length of traffic flow accurately and effectively.