Explaining Multiple Types of Crash Injury Severity Predictions with Layer-wise Relevance Propagation in Multi-task Deep Neural Networks

Abstract

Abstract Accurately predicting the severity of traffic accidents is crucial for preventing them and safeguarding traffic safety. Practitioners need to understand the underlying predictive mechanisms to identify associated risk factors and develop appropriate interventions effectively. Unfortunately, existing research often falls short in predicting diverse outcomes, with some studies neglecting the latter entirely. Moreover, designing explainable deep neural networks (DNNs) is challenging, unlike traditional models, which makes it difficult to achieve explainability with DNNs that incorporate neural networks. We propose a multi-task deep neural network framework designed to predict different types of injury severity, including injury, fatality, and property damage. Our proposed approach offers a thorough and precise method for analyzing crash injury severity. Unlike black-box models, our framework can pinpoint the critical factors contributing to injury severity by employing improved layer-wise relevance propagation. Experiments on Chinese traffic accidents demonstrate that our model accurately predicts the factors associated with injury severity and surpasses existing methods. Moreover, our experiments reveal that the critical factors identified by our approach are more logical and informative compared to those provided by baseline models. Additionally, our findings can assist policymakers make more enlightened decisions when devising and implementing improvements in traffic safety.