Emissions-reduction strategy for connected autonomous vehicles on mixed traffic freeways

Abstract

Acceleration and deceleration behaviors negatively affect car-following dynamics of vehicles on freeways, leading to traffic oscillations, thus increasing transportation emissions. The emergence of connected autonomous vehicles (CAVs) offers a potential to mitigate these effects. This paper aims to propose a car-following strategy for CAVs to reduce transportation emissions on mixed traffic freeways. Firstly, we adopted the calibrated Gipps model to characterize car-following behaviors of human-driven vehicles (HDVs). Based on this, we proposed a car-following strategy for CAVs designed to mitigate traffic oscillations and reduce transportation emissions. Subsequently, we introduced a simulation framework for analyzing transportation emissions. Finally, considering foggy weather as a practical application, we validated the effectiveness of our proposed CAV car-following strategy, by evaluating transportation emissions under different conditions, including three emission categories, varying CAV market presentation rates (MPRs), two foggy levels, and four speed limits. The results show a close correlation between transportation emissions and traffic oscillations, indicating that increased oscillations result in higher emissions. The proposed CAV strategy demonstrates significant efficacy in mitigating traffic oscillations and reducing transportation emissions in foggy weather. As the MPR of CAVs equipped with our strategy increases, transportation emissions decrease gradually. Specifically, at an MPR of 1, the average reductions in CO, HC, and NO_x emissions reach 16.99 %, 11.65 %, and 20.82 %, respectively. Following findings from our analysis, recommended insights are provided for speed limit strategy and CAV dedicated lane management for mixed traffic on foggy freeways, from the perspective of reducing emissions.