Road Traffic Accident Risk Prediction and Key Factor Identification Framework Based on Explainable Deep Learning

Abstract

The prediction and identification of key factors in road traffic accidents are crucial for accident prevention, yet previous studies have often examined these aspects separately. To comprehensively assess the risk level of road traffic accidents and their key determinants, this paper proposes a comprehensive forecasting and analysis framework that offers a novel perspective for identifying key risk factors from a modeling standpoint compared to existing methods. The CNN-BiLSTM-Attention model was developed for predicting the risk value of road accidents, and DeepSHAP was employed to interpret the model and extract the key factors contributing to traffic accidents. This deep learning framework combines convolutional neural networks (CNN) and Bi-directional long short-term memory (BiLSTM), while incorporating a spatial-temporal local attention mechanism to enhance its capability in capturing spatiotemporal features. Through analysis and experimentation on real-world datasets, our model demonstrates superior accuracy in predicting traffic accident risk compared to the benchmark model, achieving a Mean Absolute Error (MAE) of 0.2475 on the UK dataset and 0.2683 on the US dataset. The results obtained from DeepSHAP were found to be more rational and informative in identifying key factors of different severity levels using four methods. To verify the rationality and stability of obtaining these key factors, the first 15 factors were reintegrated into the prediction model, resulting in almost unchanged accuracy and reduced model iteration time. By improving the influential factors, road traffic accidents can be effectively mitigated.

Keywords

• Traffic accidents
• risk prediction
• explainability
• deep learning