Correlation Analysis and Prediction of Pedestrian Casualty Risk in Car-Pedestrian Collision Accident

A model for predicting the risks of pedestrian injuries after accidents based on clustering method and back-propagation neural network was proposed to study the factors that affect pedestrian injuries in car-pedestrian collision accidents. Firstly, the data of 372 car-pedestrian collision accidents between 2018 and 2019 in the National Vehicle Accident In-depth Investigation System (NAIS) database was collected as the research object. And it was statistically analyzed to obtain 9 accident characteristic parameters in three dimensions of vehicle, pedestrian and collision status. Then, combined with the characteristics of each accident, the K-means clustering method was selected for continuous eigenvalues, and the hierarchical clustering method was selected for discrete eigenvalues to obtain the correlation between pedestrian injury and death risks and various characteristic parameters. Finally, a BP neural network prediction model based on accident characteristics was established to predict pedestrian injuries and deaths. The test results show that the success rate of the pedestrian casualty risk prediction model is 86%.