SYNOPSIS
Connected and autonomous vehicles (CAVs) can activate the adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) system when it follows another human driving vehicle (HDV) and CAV, respectively. This could significantly change the car-following behaviors and affect the performance of the intersection systems. As it is expected to have a long transition time during which HDVs and CAVs will coexist, an intensive evaluation of the impacts of CAVs in signal intersection systems, as along with an in-depth analysis on intersection capacity adjustments that consider varying market penetration rates (MPRs) of CAVs, is highly needed. To better prepare and guide both intersection planning and operations under different MPRs of CAVs and traffic demands, this research estimates the lane-level and intersection-level capacity. In the lane-level capacity investigation, adjustment factors for saturation headway and saturation traffic flow rate for each lane under different MPRs of CAVs are investigated. In the intersection-level capacity investigation, the maximum throughput function for different MPRs of CAVs is calibrated. With 100% CAVs, the saturation headways for the exclusive through lane, exclusive left-turn lane, and shared-right-and-through lane decrease by 55.8%, 48.9%, and 42.4%, respectively. The maximum throughput of the intersection with 100% CAVs increases by 70% compared to the scenario with only HDVs. Moreover, the maximum throughput increases rapidly after a 60% MPR of CAVs. The framework and the capacity adjustment factors calibrated in this research could provide a reference to traffic engineers and planners for calculating the intersection capacity under different MPRs of CAVs.
ABOUT THE SPEAKER
LI SONG received his Ph.D. degree in transportation engineering from the University of North Carolina at Charlotte, M.S. degree from Harbin Institute of Technology, and B.S. degree from Shanghai Maritime University. He worked as the Research Assistant of the NC-CAV Center of Excellence on Connected and Autonomous Vehicle Technology and the USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) in the Department of Civil and Environmental Engineering at the University of North Carolina at Charlotte. His research interests include traffic safety, traffic control, and intelligent transportation systems.