Analyzing Emissions and Energy Efficiency at Unsignalized Real-world Intersections Under Mixed Traffic Control

Michael Villarreal, Dawei Wang, Jia Pan, Weizi Li
11/20/2023
cs.CYcs.AI

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Abstract

Greenhouse gas emissions have dramatically risen since the early 1900s with U.S. transportation generating 28% of U.S. emissions. As such, there is interest in reducing transportation-related emissions. Specifically, sustainability research has sprouted around signalized intersections as intersections allow different streams of traffic to cross and change directions. Recent research has developed mixed traffic control eco-driving strategies at signalized intersections to decrease emissions. However, the inherent structure of a signalized intersection generates increased emissions by creating frequent acceleration/deceleration events, excessive idling from traffic congestion, and stop-and-go waves. Thus, we believe unsignalized intersections hold potential for further sustainability improvements. In this work, we provide an emissions analysis on unsignalized intersections with complex, real-world topologies and traffic demands where mixed traffic control strategies are employed by robot vehicles (RVs) to reduce wait times and congestion. We find with at least 10% RV penetration rate, RVs generate less fuel consumption, CO2 emissions, and NOx emissions than signalized intersections by up to 27%, 27% and 28%, respectively. With at least 30% RVs, CO and HC emissions are reduced by up to 42% and 43%, respectively. Additionally, RVs can reduce network-wide emissions despite only employing their strategies at intersections.

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@article{villarreal2023analyzing,
  title  = {Analyzing Emissions and Energy Efficiency at Unsignalized Real-world Intersections Under Mixed Traffic Control},
  author = {Michael Villarreal and Dawei Wang and Jia Pan and Weizi Li},
  year   = {2023},
  eprint = {2311.11866},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.11866v2}
}

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