Car-following strategy of connected automated vehicles for reducing particulate matter (PM) emissions on foggy freeways

Particulate matter (PM) emissions, recognized as the most reliable indicator of negative human health effects associated with air pollution, tend to increase in foggy weather due to unstable car-following behavior of human-driven vehicles (HDVs) on freeways. Compared with HDVs, connected automated vehicles (CAVs) exhibit better stability characteristics and have potential for reducing PM emissions. Therefore, this paper focuses on proposing a CAV car-following control strategy by enhancing traffic flow stability to reduce PM emissions in foggy weather. Firstly, we employed a calibrated Gipps model for characterizing HDVs’ behavior on foggy freeways. With the theory of traffic flow stability as a foundation, we proposed the CAV strategy to mitigate speed fluctuations and reduce PM emissions. Finally, simulation experiments were conducted to assess the effectiveness of the proposed CAV strategy under different conditions, including two fog intensity levels, four speed limits, and varying CAV market penetration rates (MPRs). The results reveal an intrinsic relationship between speed fluctuations and PM emissions in foggy weather. The CAV strategy reduces PM emissions by mitigating speed fluctuations. Specifically, PM emissions decrease as the CAV MPR increases. At a 100% MPR, the average PM emissions reduce by 34.15% in light fog and 24.42% in heavy fog. Findings from our analysis provide insights into CAVs PM emissions reduction from the perspective of traffic flow stability enhancement on foggy freeways.