Quantifying air quality benefits resulting from few autonomous vehicles stabilizing traffic

Stern, Raphael; Chen, Yuche; Churchill, Miles; Wu, Fangyu; Monache, Maria Laura Delle; Piccoli, Benedetto; Seibold, Benjamin; Sprinkle, Jonathan; Work, Daniel B. · 2018 · OpenAlex-citations

DOI: 10.1016/j.trd.2018.12.008

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Summary

This study investigates the potential for autonomous vehicles (AVs) to reduce vehicular emissions by stabilizing traffic flow, specifically by dampening stop-and-go waves generated by human drivers. The research addresses the question of whether a low penetration rate of AVs (approximately 5%) can significantly lower emissions for the entire traffic fleet, independent of long-term changes in land use or travel demand. The motivation stems from the understanding that traffic instabilities, such as phantom jams, increase fuel consumption and pollutant emissions, and that AVs equipped with specific control strategies could mitigate these oscillations. The researchers utilized experimental data from field tests conducted on a circular ring road in Tucson, Arizona, involving 21 to 22 vehicles. In these experiments, a single autonomous-capable vehicle (the CAT Vehicle) was integrated into a stream of human-piloted vehicles. Three distinct control strategies were tested to dampen traffic waves: a "FollowerStopper" controller, a trained human driver instructed to maintain a constant speed, and a Proportional-Integral (PI) controller with saturation. Vehicle trajectory data, including velocity and acceleration, were collected via computer vision algorithms. Emissions were estimated using the EPA’s MOtor Vehicle Emissions Model (MOVES), employing a Vehicle-Specific Power (VSP) analysis to account for transient driving dynamics. The study evaluated four fleet scenarios, ranging from the experimental fleet composition to projected US fleets in 2030 and 2050, to assess the robustness of the findings across different vehicle mixes and electrification levels. The results demonstrate that actively dampening traffic waves with a single AV significantly reduces emissions for the entire fleet. When stop-and-go waves were present, emissions were higher due to increased acceleration variability. Upon AV intervention, emissions reductions ranged from 15% for carbon dioxide to 73% for nitrogen oxides. Specifically, in the experimental fleet scenario, hydrocarbon emissions decreased by 51.5%, carbon monoxide by 39.1%, and nitrogen oxides by 73.5%. The MOVES analysis revealed a shift from higher to lower operating modes during AV control, indicating reduced engine demand. These benefits were observed across all tested control strategies and fleet scenarios, confirming that the reduction is driven by traffic stabilization rather than specific vehicle characteristics. The significance of this work lies in providing experimental evidence that even a small fraction of autonomous vehicles can yield substantial air quality benefits by smoothing traffic flow. Unlike previous simulation-based studies, this research validates the hypothesis using real-world data, showing that AVs can positively impact the emissions of surrounding human-driven vehicles. The findings suggest that early deployments of AVs designed for traffic stability could offer immediate environmental benefits, particularly in congested conditions prone to stop-and-go waves, before widespread adoption alters broader transportation patterns.

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• traffic density