An Analysis of Traffic Accident Severity Based on Mutual-information Bayesian Network

The methods of mutual information and Bayesian network are conducted to develop a model to grasp the factors affecting the severity of accidents in the inter-provincial bus industry. The quantitative interaction between changes in factors and the severity of accidents are analyzed. Given the limitation of the samples' size of the industry and the subjectivity of experts' knowledge of modeling, an improved discrete algorithm is used for data mining.A primary network construction method combining mutual information and cross-validation is proposed. Taking model analysis with 741 inter-provincial bus accidents in Shanghai from 2005 to 2019 as a case study, the results show that the most sensitive influencing factors of accidents are gender, weather, and vehicle type."Female driver""snow, wind, and fog""medium-size bus"account for 13.5%, 8.8%, and 5.7% of the weight of the accidents, respectively. Additionally, drivers' age has little contribution to the misfortune of group death and injury. Bus size has non-monotonic relationships with safety. The probability of more than seven people being injured during 00:00 to05:00 rises by 9%. The factors of season, weather, and time are not directly related to property loss. The generalization ability of the constructed model is better than other comparable models. The average AUC is 0.644 588, and the hit rate reaches 97.3%.