A Capacity Modelling Study of Diverging Areas in Urban Tunnels Under Intelligent Connected Environments

With the advancement of connected automated vehicles (CAVs), their increasing presence in traffic flows is expected to reshape road capacity. In urban tunnels, diverging areas are essential for traffic efficiency and safety. However, existing research has mainly focused on merging and diverging areas in expressways and arterial roads, with limited attention to urban tunnel environments. To address this gap, this study proposes a capacity estimation method for the diverging areas in urban tunnels under an intelligent connected environment. A simulation framework was developed to analyse key influencing factors under different CAV penetration rates. Then, a capacity estimation model was established using a multivariate nonlinear fitting approach and was validated through SUMO simulation experiments. The results indicate that: (1) penetration rate, diversion ratio and deceleration lane length are key parameters influencing capacity; (2) a CAV penetration rate of 0.5 marks a threshold beyond which deceleration lane length has little impact; and (3) a case study using the Nanping Tunnel in Chongqing shows that the model’s prediction error is within 5.29%. This study offers a targeted capacity estimation method for diverging areas in urban tunnels, aiding adaptive traffic control design and CAV deployment planning.