Prediction and analysis of expressway tunnels crash based on modified convolutional neural network and Shapley additive explanations

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The prediction and analysis of traffic crashes in expressway tunnels plays a pivotal role in enhancing tunnel safety. A modified convolutional neural network (M-CNN) for tunnel traffic crash prediction was developed in this study. The synthetic minority over-sampling technique was used to address the issue of imbalanced crash data. Based on the prediction results, sections of high risk in tunnels were identified and Shapley additive explanations (Shap) were used to enhance the interpretability of the M-CNN. The results showed that the prediction accuracy of the M-CNN is high (74.62%) and surpassed the accuracy of baseline models (convolutional neural network, back-propagation neural network, random forest, long short-term memory and support vector machine). The tunnel entrance and exit sections were identified as risk zones. In addition, driver’s operation, tunnel grade and vehicle speed were found to have the greatest impact on rear-end crashes, sideswipe crashes and hitting guardrail crashes, respectively. This study also revealed intricate interaction effects between the variables and the skidding resistance index, with this index exhibiting a negative correlation with crash risk. The research findings have significant implications for the future implementation of machine learning models in crash studies, with practical applications for reducing crash rates.