Using Automated Vehicle (AV) Technology to Smooth Traffic Flow and Reduce Greenhouse Gas Emissions

Almatrudi, Sulaiman; Parvate, Kanaad; Rothchild, Daniel; Vijay, Upadhi · 2022 · ROSA P / University of California, Berkeley. Institute of Transportation Studies

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Summary

This research report investigates the potential of Automated Vehicle (AV) technology, specifically "flow smoothing," to mitigate traffic congestion and reduce greenhouse gas (GHG) emissions. The study is motivated by the fact that passenger and heavy-duty vehicles account for 36% of California’s GHG emissions. Human driving behaviors in congested conditions often create stop-and-go traffic waves, which necessitate repeated braking and accelerating, significantly increasing fuel consumption. Flow smoothing utilizes adaptive cruise control algorithms to dissipate these waves by maintaining constant speeds and larger headways, thereby improving system-wide fuel efficiency. The authors conducted experiments using the SUMO traffic microsimulation software, modeling a half-mile segment of the I-210 highway in Los Angeles. They implemented the "FollowerStopper" controller, a flow-smoothing algorithm, and simulated traffic with varying penetration rates of AVs (0%, 10%, 20%, and 30%). To ensure realistic results, the study calibrated vehicle distributions based on 2018–2019 California registration and sales data, categorizing vehicles into five classes including passenger cars, heavy-duty vehicles, and zero-emission vehicles. Emissions were estimated using the HBEFA model, with fuel economy serving as a proxy for CO2 emissions. The study also performed sensitivity analyses to evaluate the impact of aggressive human driving behaviors and different vehicle types on flow smoothing effectiveness. The results demonstrate that flow smoothing significantly improves fuel economy. Increasing the proportion of flow-smoothing AVs from 0% to 30% nearly doubled the average miles per gallon (MPG) of non-AV human-driven vehicles, rising from 19 MPG to 36 MPG. This aligns with prior empirical findings suggesting a 40% reduction in fuel consumption. However, sensitivity analyses revealed that aggressive lane-changing behavior by human drivers attenuates these benefits; when 50% of drivers behaved aggressively, the maximum fuel economy dropped to 26 MPG. Additionally, while internal combustion engine vehicles saw substantial improvements, electric vehicles experienced only a 25% improvement due to regenerative braking, which already mitigates some energy losses from traffic waves. The study concludes that while flow smoothing offers significant environmental benefits, private sector incentives are insufficient for widespread deployment because the primary gains are system-level rather than individual. The authors propose policy mechanisms, such as modifying the Safer Affordable Fuel Efficient (SAFE) Vehicles Rule, to award automakers off-cycle credits for CO2 reductions achieved through flow smoothing. They also highlight the need for further research into realistic human driving models to ensure robust controller performance. The report serves as a blueprint for policymakers, emphasizing that flow smoothing is a viable strategy for decarbonizing transportation, provided that regulatory frameworks support its integration into commercially available Level 2 autonomous systems.

Key finding

Deploying flow-smoothing automated vehicles increased the average fuel economy of human-driven vehicles from 19 to 36 miles per gallon, though this benefit was significantly reduced when a high proportion of human drivers exhibited aggressive lane-changing behavior.

Methodology

modeling

Provenance

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Topics

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• traffic density