North Carolina Department of Transportation
Research & Innovation Summit – 2021

Investigating Operational Performance of Connected and Autonomous Vehicles on Signalized Superstreets

Authors: Shaojie Liu, Wei Fan
University of North Carolina at Charlotte
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Background and Research Objectives

The evolution of information technology and computing power has made CAVs no longer a theory but a foreseeable reality. To mitigate the research gap between CAVs and superstreet, this research attempts to investigate the environmental impact of CAVs in the superstreets with consideration of platooning and trajectory planning capabilities featured by CAVs. A real-world signalized superstreet is selected for the simulation-based study. Wiedemann 99 (W99) and intelligent driver model (IDM) car following models are employed to represent the human driven vehicles (HDV) traffic and connected and autonomous vehicle (CAV) traffic, respectively. The W99 model for HDV traffic is calibrated based on reported traffic flow characteristics using the Genetic Algorithm (GA). To better understand the potential environmental implication of CAVs in the superstreet, platooning and trajectory planning are employed for CAVs specifically considering the connectivity between vehicles and infrastructures. To capture the adoption process of CAV technologies, this research also evaluates the impacts of CAVs under different market penetration rates, along with different traffic scales.