Tunnel Traffic Evolution during Capacity Drop Based on High-Resolution Vehicle Trajectory Data

Abstract

Capacity drop is the critical phenomenon that triggers traffic congestion, while traffic evolution is very complex during a capacity drop. This study applied the empirical vehicle trajectory data to explore the traffic characteristics during the capacity drop at the tunnel bottleneck section. We first construct a capacity drop analysis model using image processing technology to extract high-precision vehicle trajectories. We then analyze the characteristics of the evolution process of the capacity drop at the bottleneck area. The results show that the capacity drop is a dynamic evolution process from free flow to congested flow where traffic operation is distinct. The capacity drop shows the difference between congested flow and non-congested flow. The driving characteristics of drivers in the two states are also different. The influence of lane-changing behavior on the capacity drop is estimated. In the free flow state, the disturbance caused by lane-changing can be quickly eliminated. With the increase in vehicle numbers in the area, the frequent lane-changing behavior accumulates disturbance. When the disturbance reaches a certain degree, congestion will occur, and the vehicle’s speed will drop sharply, resulting in a capacity drop.