Deep Forest with SHapley additive explanations on detailed risky driving behavior data for freeway crash risk prediction

Abstract

Freeway crash risk prediction is a critical component of traffic safety management, yet existing crash risk models often fail to capture complex driving behaviors and lack interpretability. This study introduces a novel freeway crash risk prediction framework based on the Deep Forest (DF) algorithm, considering the detailed risky driving behavior data. The DF model integrates multi-grained scanning and cascade forest layers, enabling it to capture the complex relationship between risky driving behavior features. SHapley Additive Explanations (SHAP) are applied to interpret the model's predictions, including both SHAP summary and interaction results. Additionally, ablation studies are conducted to evaluate the contributions of key components like multi-grained scanning and cascade structures to the model's performance. The experimental results demonstrate that the DF model outperforms traditional machine learning models. The DF model achieves an area under the receiver operating characteristic curve of 0.825, with a balanced Sensitivity of 0.75 and Specificity of 0.816, surpassing other models. The ablation studies show that removing multi-grained scanning, cascade layers, or completely random tree forest leads to performance declines, confirming the importance of each component. The SHAP analysis highlights that sharp acceleration and braking behaviors have the most significant impact on crash risk, offering clear, interpretable insights into how driving behaviors contribute to risk. Overall, the DF model's superior performance and SHAP-based interpretability provide a powerful tool for traffic safety management. These findings emphasize the value of incorporating both driving behavior intensity and model interpretability into crash risk prediction, offering practical applications for reducing crash rates.