Analyzing the Impact of Geometric Design on Vehicle Operational Speed and Trajectory in Expressway Tunnels: Integrated Model Based on XGBoost-SHAP Method

Abstract Optimized geometric design of a tunnel is crucial for enhancing its operational efficiency as well as assessing traffic safety. This study utilizes a continuous prediction model employing a three-segment feature aggregation method that considers upstream, current, and downstream segments to estimate driver behavior at any position within the tunnel. The geometric features of different positions, ranges, and quantities were compared to obtain appropriate geometric features that reduce the features’ deviation from the driver’s perception. Through orthogonal experimental design, training road sections were developed incorporating horizontal and vertical alignment combinations. An integrated extreme gradient boosting–Shapley additive explanation method (XGBoost-SHAP) model was proposed to predict operational speed, acceleration, and trajectory deviation at arbitrary segments in the tunnel. The proposed three-segment prediction model outperforms traditional models considering only upstream and downstream segment, achieving higher prediction accuracy. Compared to conventional approaches, the XGBoost-SHAP model reduced the root mean square error by 8% on the validation tunnel. Key factors are identified influencing vehicle operational speed include the average curvature from 400 to 200 m upstream, the average absolute curvature and the grades from 200 m upstream to the current segment, and the average curvature and average grade from the current segment to the farthest visible downstream. Results indicate that vehicle operational speed is mainly influenced by the gradient of the current segment. Vehicle acceleration rate is influenced by the gradient changes in current segment. Vehicle trajectory deviation is impacted by curvature in the current and downstream segments. Recommendations are further proposed for geometric design of expressway tunnels. These insights are pivotal for evaluating and enhancing tunnel designs to optimize traffic efficiency.