Assessing the impacts of connected-and-autonomous vehicle management strategy on the environmental sustainability of urban expressway system

Abstract

Urban expressway network has become essential for over 30% of road trips in urban areas, making them significant in shaping environmental sustainability. Recently connected-and-autonomous vehicle (CAV) technology is expected to significantly transform the urban expressway systems, but little attention has been attributed to quantitatively assessing the impacts of managing CAV in mixed traffic flow on urban expressway environmental sustainability. To address this research gap, this study proposes a comprehensive simulation-optimization framework. The simulation component involves multiclass cell transmission model that simulates and evaluates the traffic flow of mixed human-driven vehicles and CAVs on the expressway, whereas the optimization component proposes a novel management strategy that jointly considers the dedicated lanes and speed management. An analytical tool is developed and validated in North-South Elevated Expressway in Shanghai, China, and further investigated the sensitivity of environmental impacts to vehicle headway, travel demand, and CAV market penetration rate. The results indicate that use of the proposed tool can reduce the total fuel consumption and exhaust emissions compared to baseline scenario. Overall, the insights from this study are valuable for policymakers of transportation in future cities, who develop management strategies on urban expressways that enhance the potential impact of CAVs on environmental sustainability.