Optimized structure learning of Bayesian Network for investigating causation of vehicles’ on-road crashes

Abstract

A vehicle's crash can be seen as a failure of microscopic road transportation system. The causal investigation of vehicles’ crashes has drawn much attention from academia and industry alike, which is of significance to road traffic safety. This study develops a structure learning method to construct Bayesian Network (BN). The BN as generated by the method can comprehensively illustrate the causal relationships between risk contributing features and vehicles’ on-road risky events (i.e. near-crash and crash). The proposed structure learning method has following three advantages: (1). considering multiple categories of features; (2). applying robust feature selection method to improve prediction performance and facilitate the explanation of causation; and (3). making a trade-off between the complexity and interpretability of BN structure. The method is applied on the Second Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) database for case study. The results show that the generated optimal BN achieves satisfactory performances on both structure complexity and prediction accuracy. Besides, as compared to the BN built by the other state-of-the-art structure learning methods, the optimal BN presents superior performance on causal interpretability. Also, by performing causal inferences upon the optimal BN, this study examines and analyzes the contributions of several key features to the risky events. Several interesting findings about the features’ contributions are reported in this paper, which could provide valuable references for road safety engineering in the future.