United States Department of Transportation
i
Impacts of Connected and Autonomous Vehicles on the Performance of Signalized Networks: A Network Fundamental Diagram Approach
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2022-04-06
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[PDF-1.76 MB]
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Edition:Final report (2/1/2020 –12/31/2021)
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Abstract:Many eco-driving strategies through speed control using constrained optimization algorithms have proven effective on signalized roads. However, heuristic speed limit control strategies and understanding of their overall performance across congestion levels remain an unexplored topic. In this work, we systematically study the performance of an eco-driving strategy based on Vehicle-to-Infrastructure (V2I) communication via the advisory speed limit (ASL), a speed limit designed for individual vehicles based on the idea of making vehicles enter signalized intersections at saturated headway intervals. The theoretical performance of our algorithm to vehicle trajectories is analyzed across different congestion levels. By simulating with the BA Newell’s car-following model, the simplified Gipps model, and the Krauss model, calculated network fundamental diagrams (NFDs) and results of the Virginia Tech Microscopic Energy and Emission (VT-micro) model reveal an improvement in system mobility by nearly 10% and a reduction in fuel consumption by up to about 45% in the saturated condition. We further consider different market penetration rates (MPRs) and ASL implementation areas and show our algorithm can lead to about 35% fuel consumption reduction even with a 10% MPR. We recommend an ASL implementation area of about 100 meters, which can well balance the algorithm efficacy and computation cost.
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