Study on the Design of Variable Lane Demarcation in Urban Tunnels

Abstract

In order to alleviate the influence of low-speed vehicles on tunnel safety, this paper discusses the setting method of variable lane boundaries in urban tunnels. VISSIM simulation software is used to analyze the influence of low-speed vehicles on tunnel traffic flow when lane changes are allowed and when lane changes are prohibited. The results show that the influence of low-speed vehicles on the average speed of traffic flow in urban tunnels is the greatest, and the influence of low-speed vehicles on the average speed of traffic flow can be significantly alleviated when lane changes are allowed in the lane dividing line. When the speed of low-speed vehicles is 40 km/h and the variable lane is set, the average delay time is reduced by 30–50%. The existence of low-speed vehicles significantly increased the average delay time of the local lane, and the lower the vehicle speed and the greater the road traffic volume, the longer the average delay time. When the speed of low-speed vehicles is 40 km/h and the traffic volume is 1200 pcu/h, the traffic density of the right-hand lane decreases by 43.5% after the variable lane is set. While lane changing is prohibited, the presence of low-speed vehicles causes a backlog of vehicles in the rear of the lane, which leads to a significant increase in traffic density. Setting lane-changing permits can alleviate the impact of low-speed vehicles on traffic flow. The research results can provide a scientific basis for the operation and management of urban tunnels.

Research on Calculating Traffic Capacity in Extra-Long Subsea Tunnels—A Case Study of the Qingdao Jiaozhou Bay Subsea Tunnel

“…Due to the high data requirements of these methods and the lack of sufficient data, many scholars have turned to simulation-based capacity models. For instance, Beheshtitabar et al [20] used a VISSIM model to investigate the impact of increased look-ahead distance at the Hampton Road Bridge Tunnel in Virginia, while Fang et al [21] developed an urban tunnel capacity model based on VISSIM and analyzed the impact of variable lanes on capacity. Ye et al [22] analyzed the impact of driverless driving on capacity using a road cellular automaton model based on the two-state safety speed model, and Winkler and Fan [23] built a capacity model for high-speed traffic paths based on VISSIM simulations to evaluate the effects of lane restrictions, driver behavior, and interchange density on expressway capacity.…”

“…The paper discusses the various methods that have been used in existing traffic capacity model research and divides them into three categories [14][15][16][17][18][19][20][21][22][23]: theoretical analysis, data mining, and simulation. Table 1 provides a comparison of the three methods.…”