Deep Learning Model for Crash Injury Severity Analysis Using Shapley Additive Explanation Values

Abstract

Analysis of traffic crash and associated data provides insights and assists with identification of cause-and-effect relationships with crash probabilities and outcomes. This study utilized eight years of police-reported Nebraska crash data using a deep neural network (DNN) to model crash injury severity outcomes. Prediction performances and model interpretability were examined. The developed DNN excelled in prediction accuracy, precision, and recall but was computationally intensive compared with a baseline multinomial logistic regression model. While the lack of interpretability power of deep learning models limits their usage, the adoption of SHapley Additive exPlanation (SHAP) values was an improvement. Conclusions drawn from the DNN model are generally consistent with the estimated baseline model. For instance, the variable total number of pedestrians was found significant in both scenarios of the multinomial logit model indicating a strong relationship with more severe crash injury outcomes. It was also found important in all three sets of parameters in DNN. SHAP values also allow in-depth analysis of prediction results on a single observation, such as the variable crash type (same direction sideswipe) contributing to classifying a single observation as property damage only. These findings are beneficial for making more informed transportation safety-related decisions.