# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 4 Discussion  
Recent research has demonstrated that intelligent transportation systems have greatly benefited from data-driven approaches to traffic safety. Our findings on driver behavior patterns in mountain roads align with emerging studies showing that behavioral analysis is crucial for accident prevention. Traditional models are giving way to more sophisticated neural networks like LSTM, which can better handle the temporal dependencies inherent in traffic data and capture the complexity of driving environments that leads to frequent traffic crashes. The integration of multiple behavioral indicators (speed, acceleration, headway, and steering) provides a more comprehensive understanding of risk factors than single-parameter analyses. The application of the FP-Growth algorithm for association rule mining represents a significant methodological advancement over traditional statistical approaches. Figuring out the potential correlation between various risk factors and revealing their coupling mechanisms are effective ways to explore and identify crash causes. Our methodology aligns with recent innovations in data mining applications for traffic safety. These advancements have enabled us to identify complex behavioral patterns that might be missed by conventional analysis methods. The LSTM neural network model employed in our study parallels recent developments in traffic prediction systems. The practical significance of our research lies in its potential applications for enhancing mountain road safety. The identification of specific high-risk behavioral combinations, particularly the \"speeding + short headway + frequent lane change\" pattern, provides concrete targets for intervention. This finding corresponds with research by [PERSON] et al. [44], which utilized a graph CNN-LSTM neural network for short- and long-term traffic forecasting based on trajectory data, demonstrating the value of trajectory-based models for safety enhancement. Figure 13: ROC curve (**left**) and PR curve (**right**) of LSTM traffic conflict prediction model on the test set. Recent studies on driving behaviors on curved road sections by various researchers have highlighted how vehicle trajectories are comprehensively affected by the interactions between the vehicle, the driving behavior, and the road environment. Our identification of specific curve entry and exit behaviors aligns with these findings, providing quantitative evidence for the development of targeted safety countermeasures. The predictive model developed in this study offers practical tools for real-time risk assessment. As demonstrated by [PERSON] et al. [45], traffic flow prediction is important for urban planning and traffic congestion alleviation, as well as for intelligent traffic management systems. Our LSTM-based prediction system extends these applications to the specialized context of mountain roads with complex geometries. Building on our findings, several promising directions for future research emerge. The integration of weather data with behavioral patterns could enhance prediction accuracy. Future studies should also explore the potential of incorporating vehicle-to-infrastructure communication systems to provide real-time warnings based on predicted conflict risks. This approach aligns with emerging Intelligent Transportation System applications that combine predictive analytics with active safety interventions. The optimization of data mining algorithms for traffic safety analysis represents another valuable research direction. Adapting these optimized algorithms to process continuous streams of driving behavior data could enable more efficient real-time risk assessment systems. Limitations of our current study include the sample size (30 drivers), which may not fully represent the diversity of driving behaviors in mountain regions. Additionally, our data collection was limited to specific road segments in Hubei Province, China, such that the variations present in mountain roads globally may not have been captured.