United States Department of Transportation
i
Trajectory Optimization of Connected and Autonomous Vehicles (CAVs) at Signalized Intersections
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2020-09-01
[PDF-1.00 MB]
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Corporate Contributors:University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
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Edition:Final
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Abstract:This research will develop guidelines and recommendations for estimating and predicting intersection efficiency in the presence of connected and autonomous vehicles (CAVs), and therefore will lead to a better understanding of how CAVs will improve mobility at signalized intersections. To better understand the impact of CAVs on the operation of signalized intersections, autonomous vehicles (AVs) are also involved in this study, so that a mixed traffic environment can be investigated including regular vehicles, autonomous vehicles (AVs), and CAVs. A case study is conducted with a signalized intersection in Charlotte, North Carolina. The selected signalized intersection is simulated in VISSIM, a traffic microsimulation tool, to explore the impact of CAVs on the intersection. A speed advisory strategy is proposed to optimize CAVs’ trajectory approaching the intersection. Simulation results are discussed in details. Overall, the results of this study can help traffic engineers and stakeholders better understand how different market penetration levels of CAVs influence traffic operation of signalized intersections and improve efficiency of signalized intersections.
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