mlsanityData to 7 Oct 2026

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

arXiv 2311.11866 ↗First posted 20 Nov 2023cs.CY cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 31 references that are in the map. First posted on arXiv 20 Nov 2023.

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4 of 31 references.

References · 4

  1. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 774
  2. 2017Flow: A Modular Learning Framework for Mixed Autonomy Trafficcited by 67
  3. 2022Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersectionscited by 15
  4. 2022Learning Eco-Driving Strategies at Signalized Intersectionscited by 9

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