An Accident Severity Classification Model Based on Multi-Objective Particle Swarm Optimization

Abstract

Reducing accident severity is an effective way to improve road safety. In the literature of accident severity analysis, two main disadvantages exist: most studies use classification accuracy to measure the quality of a classifier which is not appropriate in the condition of unbalanced dataset; the other is the results are not easy to be interpreted by users. Aiming at these drawbacks, a novel multi-objective particle swarm optimization (MOPSO) method is proposed to identify the contributing factors that impact accident severity. By employing Pareto dominance concept, a set of Pareto optimal rules can be obtained by MOPSO automatically, without any pre-defined threshold or variables. Then the rules are used to form a non-ordered classifier. A MOPSO is applied to discover a set of Pareto optimal rules. The accident data of Beijing between 2008 and 2010 are used to build the model. The proposed approach is compared with several rule learning algorithms. The results show the proposed approach can generate a set of accurate and comprehensible rules which can indicate the relationship between risk factors and accident severity.