A Traffic Guidance Mechanism for En-route Incidents Considering Driver Behavioral Responses

Existing traffic guidance strategies are predominantly developed under homogeneous assumptions within simulation environments, lacking consideration of real-world driver behavioral response mechanisms. Consequently, they fail to meet the requirements of emergency traffic management in disaster-prone areas. To address this gap, this study takes the Tibet Autonomous Region as a case area, systematically analyzing driver behavioral characteristics and their responses to guidance information under disaster conditions. A dynamic traffic guidance framework is proposed based on event feature recognition and behavioral response characteristics across multi-hazard scenarios. Using geological hazard and traffic blockage data, the Gaussian mixture model (GMM) is employed to identify event-blockage characteristics and duration distributions. Categorizing en-route incidents into three types: complete blockage, conditional passage, and temporary control. Based on driver survey data, the Apriori association rule algorithm and structural equation model (SEM) are utilized to quantify drivers'responses to information cognition, types of guidance information, release locations, and waiting times. The results show that the path coefficients from information cognition to situational assessment and from situational assessment to behavioral response are 0.688 (p =0.07) and 0.635 (p =0.05), respectively, indicating that guidance information significantly and positively affects driver decision-making. Drivers'tolerance threshold for continuous interruptions is approximately 3 h, and their demand for guidance information exhibits a three-tier hierarchical structure, with primary attention given to event type, congestion distance, and optimal driving route. The combination of short messaging service (SMS) and quick response (QR) code distribution proves to be the most effective means of information delivery. On this basis, a"spatially hierarchical-temporally progressive"dynamic guidance strategy framework is established, consisting of three modes: route-level (long-distance diversion), segment-level (node control), and temporary-passage-level (short-term response), integrated with an emergency priority mechanism. Finally, validation using a traffic simulation platform for conditional passage events involving single-lane closures demonstrates that the proposed dynamic signal-control strategy reduces average waiting time by 21.5% compared to manual control, thereby significantly improving traffic operational efficiency under disaster conditions.