Coupling Effects of Heterogeneous Traffic Flow and Facility Design on Capacity of Tunnel Group Sections in CAV Environment

Abstract

With the advancement of connected and autonomous vehicle (CAV) technology, the traffic flow in highway tunnel group sections has transformed into a heterogeneous mix of CAVs and human-driven vehicles (HDVs). The operational capacity of these sections is influenced by multiple interdependent factors, including traffic composition, speed limit control, and tunnel configuration. Understanding the coupling mechanisms among these factors is essential for enhancing both efficiency and safety. In this study, we first analyze the characteristics of heterogeneous traffic flow under CAV conditions and the structural features of various tunnel group layouts. An integrated simulation model is then developed by combining an enhanced NaSch cellular automaton model with the Intelligent Driver Model (IDM), capturing the dynamic interactions among heterogeneous traffic, speed limits, and tunnel geometry. Through multi-scenario simulations, the individual and coupled influences of these factors on tunnel group capacity are systematically examined. The results indicate that: (1) Increasing the CAV penetration rate improves operational capacity significantly, with capacity growth tapering off after the rate exceeds 60%; (2) Each tunnel configuration corresponds to an optimal speed limit range, and a mismatch can cause considerable capacity reduction; (3) The coupling effect of traffic composition and speed control is particularly pronounced in long-span tunnel groups, where appropriately matching CAV penetration with speed limits can maximize capacity. This study elucidates the internal coupling mechanisms governing the operational capacity of tunnel groups and offers theoretical and technical support for the refined design and intelligent management of highway tunnel sections in a CAV environment.