A Particle Swarm Optimization based Feature Selection Method for Accident Severity Analysis

Reducing accident severity is an effective mean to improve road safety level. Many researches have been done to identify the risky features which would influence the accident severity. Many risky features need to be considered when building accident severity analysis model, including driver, highway, vehicle, accident, and atmospheric factors. Some of those features are irrelevant of redundant. Using those features would decrease the performance of the prediction model and bring additional computational burden. However, there are very few researches on feature selection in accident severity analysis problem to date. In this paper, we propose a particle swarm optimization (PSO) based feature selection method for accident severity analysis. The proposed method can obtain a reduced number of feature subset from the original feature pool. In order to testify the method, the accident data of Beijing from 2008 to 2010 are used for experiment. Experimental results show the proposed PSO based feature selection method can significantly reduce the number of features while improving the classification accuracy. Moreover, it can provide better interpretation of the accident severity analysis model.