Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns

Abstract

This investigation analyses driving behaviors that lead to accidents on overly sharp mountain road curves in Nanjing Province, China. We collected information through field observations and driving simulations while analyzing key indicators like the mean speed of vehicles and spacing between vehicles. The FP-Growth algorithm was used to identify frequent behavioral patterns and measure their relationship with traffic conflicts. The findings showed that unsafe driver behavior on sharp turns was common, while the combination of “speeding–tailgating–frequent lane changing” behavior increased conflict risk by 3.7 times. A predictive LSTM neural network model was developed with driver, vehicle, road, and environmental factors. Testing on 4795 samples achieved 83.7% accuracy in foreseeing conflict risk levels. The model, which distinguishes between safety conditions and three severity levels of potential conflict, can provide the most fundamental level of safety needed. The research provides quantitative tools for improved road safety management aimed at supporting real evidence-based “safe roads” approaches.