Capacity Adjustment of Lane Number for Mixed Autonomous Vehicles Flow Considering Stochastic Lateral Interactions

Abstract

Autonomous vehicles (AVs) are revolutionizing the transportation system and necessitating a reevaluation of infrastructure capacity. Apart from their shorter headway, AVs exhibit distinct behavioral characteristics compared to human-driven vehicles (HDVs) in two key aspects: (1) significantly reduced noise levels, and (2) diminished bidirectional lateral influence. Unlike HDVs, AVs maintain closer alignment at the lane middle line and are not subject to the influence of the lateral wandering of neighboring vehicles. This is attributed to their remarkable ability to accurately predict HDV behavior. As the market penetration rate (MPR) of AVs increases, the interaction patterns of HDV flow between adjacent lanes, which traditionally lead to lateral friction and capacity loss, undergo gradual transformation. The current evaluation of capacity and adjustment factors (CAFs) based on theoretical models or simulation packages may overlook the stochastic lateral friction exhibited by mixed AV flow, resulting in potentially biased results. In this study, we develop a two-dimensional stochastic model that captures the random lateral dynamics and investigate the capacity and adjustment factors for multilane mixed AV flow. The findings demonstrate that the model effectively reproduces the lateral friction phenomenon, and as the number of lanes increases, the capacity experiences a decline of up to 20% due to lateral interactions. Moreover, the study clarifies the impact of dedicated lane assignment on the total capacity, as it hinders the propagation of lateral friction.