Resilience maintenance strategy for mixed vehicle traffic on port expressway based on lane management

Abstract

Ports serve as critical nodes in the global shipping network and vital gateways for goods circulation, requiring resilience to withstand external disruptions. Expressway connecting ports are key channels for freight transport, and their traffic conditions significantly influence port operational resilience. This study explores optimal lane management strategies for mixed traffic environments involving connected autonomous vehicle (CAV) and human-driven vehicle (HDV) on future port-connected expressway. By integrating connected autonomous truck (CAT) into the mixed traffic flow, this study proposes three lane management strategies tailored to different vehicle purposes. The intrinsic relationship between traffic flow and the penetration rate of CAV are investigated by simulation and numerical analysis. A traffic flow prediction model is constructed to evaluate traffic conditions in real time and determine optimal fleet management strategies based on a decision-making model. Experimental results demonstrate the high reliability of the proposed decision-making outcomes. Finally, sensitivity analysis examines the impacts of speed fluctuations and transition segment lengths on traffic flow under different strategies. It is found that more concentrated speed distributions lead to higher peak traffic flow, while longer transition segments minimize efficiency losses caused by strategy changes. These findings provide valuable insights for improving future traffic conditions around ports.