With the rapid expansion of highway infrastructure, effective emergency management has emerged as a critical challenge for public transportation safety. Existing resource allocation methodologies are often inadequate for addressing highway incidents, particularly in mountainous regions characterized by complex geological conditions, dynamic weather patterns, and extensive bridge and tunnel networks. This study aims to investigate the intricate relationship between highway emergencies and personnel allocation by systematically identifying the key factors influencing resource distribution. Employing a sophisticated methodological approach, this study integrates the random forest (RF) model with cost management principles to comprehensively assess the significance of various influencing factors. The Shapley additive explanations (SHAP) theory is leveraged to quantify the nuanced contributions of individual factors to emergency staffing, thereby enhancing the interpretability of the model. Through one- and two-dimensional partial dependency plots, we conducted a detailed analysis of the correlations among the critical determinants. The research findings revealed macro-level traffic dynamics and established meaningful connections between specific emergency scenarios and targeted response strategies. By providing localized decision-making insights, this study establishes a robust analytical framework that bridges highway incident characteristics with coordinated emergency responses. Finally, we propose a comprehensive framework that offers high predictive accuracy, logical transparency, and practical adaptability in the highway emergency resource allocation.

Low-grade roads often experience frequent roadside intrusions, leading to serious conflicts and disorder. Accurate prediction of the complex traffic-behavior characteristics on such roads is essential for understanding the mechanisms of traffic accidents influenced by roadside intrusions. For this purpose, we collected videos depicting five types of common roadside intrusions on low-grade highways and urban roads. From these videos, we extracted high-resolution vehicle micro-trajectories, and determined the vehicle speeds as they traversed the intrusion area. Then, we identified characteristic sections within the intrusion area, and analyzed the evolution of spatial and temporal characteristics of the vehicle speed. Finally, we established a vehicle speed prediction model using linear, logarithmic and cubic regressions. Notably, the cubic regression model exhibited superior speed prediction performance in the complex scenarios of the intrusion area. The results showed that speed reduction in the intrusion zone of low-grade urban roads is typically higher than that on highways. The deceleration effect is significant for drivers approaching the intrusion source. Additionally, drivers tend to accelerate through the front intrusion zone when their intentions align with those of the intrusion source. However, in scenarios where predicting the behavioral intentions of the intrusion source is challenging, speed may fluctuate to some extent.