Research on Spiral Tunnel Exit Speed Prediction Model Based on Driver Characteristics

The “white hole effect” alters the driving environment during a tunnel’s exit phase, making it more difficult and uncertain for drivers to access information and control their behavior, thereby endangering traffic safety. Consequently, the driving risk at the exit of a long spiral tunnel served as the subject of this study, and the Jinjiazhuang spiral tunnel served as the object of the natural vehicle driving experiment. Following the theory of a non-linear autoregressive dynamic neural network, a vehicle speed prediction model based on driver characteristics was developed for the exit phase of the tunnel, taking driver expectations and behavioral changes into account. It also classifies the driver’s behavior during the tunnel’s exit phase to assess the risk posed by the driver’s behavior during the tunnel’s exit phase and determine a dynamic and safe comfort speed. The study’s results indicate that the driver’s behavioral load changed significantly as the vehicle approached the tunnel exit. At the exit of the spiral tunnel, the vehicle’s actual speed was 71 km/h, which is below the speed limit of 80 km/h. This demonstrates that the expected change in the driver’s behavior in the tunnel exit phase was substantial. Therefore, setting the emotional safety and comfort speed so that the driver maintains a smooth comfort level in the tunnel exit phase can reduce the tunnel exit driving risk. The results of this study provide a benchmark for tunnel traffic safety and lay the groundwork for further development of vehicle risk warning settings for the tunnel’s exit phase.