# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 1 Introduction  
Mountainous roads are essential infrastructure for implementing the revitalization strategy, and they can help mountainous areas achieve high-quality development in terms of both economic and social benefits. Improving the construction of mountainous roads is of great significance in promoting industrial development and advancing urban-rural integration [1]. However, due to terrain, landform, and construction standards, traffic safety facilities on mountainous roads are generally underdeveloped. The road alignment is suboptimal, with complex and diverse traffic structures, sharp turns, and steep slopes [2, 3] continuously increasing traffic accident rates. To effectively reduce the incidence of traffic accidents and improve road safety, it is necessary to predict traffic accidents at sharp turns on mountainous roads. Previous research has established that unsafe driving behaviors significantly contribute to traffic conflicts on challenging road segments. [PERSON] et al. [4] identified key risk factors for professional truck drivers in China, including negligence and inappropriate driving times. Studies by [PERSON] et al. [5] and [PERSON] et al. [6] specifically examined mountain roads, developing risk prediction models based on traffic flows and dangerous driving behaviors. [PERSON] et al. [7] assessed overtaking maneuvers on rural two-lane roads, whileother researchers [8; 9] employed various methodologies--from driving simulators to vehicle sensors--to characterize driver behavior through parameters like speed, acceleration, and steering patterns. [PERSON] et al. [10] defined dangerous following behavior based on parameters from the intelligent driver model, while [PERSON] et al. [11] developed a lane-change traffic conflict prediction model using the XGBoost algorithm. [PERSON] et al. [12] demonstrated that traffic accidents in road tunnel clusters were primarily caused by poor driving behaviors, and [PERSON] et al. [13] used TTC and PET models as criteria for determining rear-end collisions and lane-change conflicts. These studies collectively demonstrate that behavioral combinations, rather than isolated actions, most significantly influence conflict probability. Advancements in intelligent transportation systems have enabled the development of sophisticated predictive models. Researchers have employed various approaches, including the Extreme Value Theory (EVT) model [14], proxy and Bayesian mixed gamma-GPD models [15], and machine learning techniques. [PERSON] et al. [16] used a Bayesian non-stationary Generalized Extreme Value model to evaluate car-following crash risk, while [PERSON] et al. [17] proposed a cycle-level traffic conflict prediction method using LiDAR data at signalized intersections. [PERSON] et al. [18] used roadside fine perception data to generate SSMs as input for real-time collision prediction models, and [PERSON] et al. [19] proposed a trajectory prediction method based on graph convolutional neural networks. [PERSON] et al. [20] analyzed traffic conflict indicators based on the indirect safety assessment model, while [PERSON] et al. [21] and [PERSON] et al. [22] investigated machine learning techniques for predicting traffic conflicts. These studies highlight the transition from post-event analysis to proactive prediction but demonstrate limited application in mountainous settings with their unique geometric and environmental challenges. Recent studies have successfully applied data mining techniques to traffic safety analysis. The FP-Growth algorithm has demonstrated superior capability in identifying complex behavioral patterns leading to traffic incidents. [PERSON] et al. [23] used the FP-Growth algorithm to analyze elderly pedestrian traffic accidents, while [PERSON] et al. [24] applied it to analyze SSD error data. Simultaneously, Long Short-Term Memory (LSTM) neural networks have shown promise in trajectory prediction and conflict detection. [PERSON] et al. [25] proposed a real-time vehicle trajectory prediction method using a multilayer LSTM model, [PERSON] et al.