Cluster Analysis of Freeway Tunnel Length Based on Naturalistic Driving Safety and Comfort

Abstract

The tunnel is an important component of freeway operation safety, and its classification method is the foundation of a refined management of operation safety. At present, the impact of different categories of tunnels on driver safety, comfort, and driving behavior under naturalistic driving conditions is not clear, and there is a lack of classification methods for tunnels of different lengths in their operation stages. This paper was based on the driving workload, which effectively expresses the safety and comfort of drivers. In this context, naturalistic driving experiments in 13 freeways and 98 tunnels with 36 participants were carried out. The DDTW+K-Means++ algorithm, which is suitable for drivers’ driving workload time series data, was used for a clustering analysis of the tunnels. According to the length of the tunnel, the operation-stage tunnels were divided into three categories: short tunnels (<450 m), general tunnels (450~4000 m), and long tunnels (>4000 m). The length of the tunnel had a positive correlation with the drivers’ driving workload, while there was a negative correlation with the vehicle running speed, and the range of changes in the drivers’ driving workload and operation safety risks in general tunnels and long tunnels was higher than that in short tunnels. Road and environmental conditions are important factors affecting the driving workload. The entrance area, the exit area of tunnels, and the middle area of long tunnels are high-risk sections in the affected area of the tunnel. These research results are of great significance for the operation safety management of freeway tunnels.