Affiliations: School of Information Science and Engineering, Central South University, Changsha, China
Correspondence:
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Corresponding author. Xiaoheng Deng, School of Information Science and Engineering, Central South University, Changsha, China. E-mail: dxh@csu.edu.cn.
Abstract: This paper presents a causation analysis model for traffic accident. Traffic accident is a result influenced by the interaction of various factors. Considering the characteristic of multi-dimensional and multi-layer in traffic accident data, a model which based on traffic accident historical data on the city of Guiyang in 2015 was built to find the main reasons and potential rules of traffic accidents. The model starts from the four main dimensions such as the drivers, the vehicles, the time-address and the environment, and uses a way which based on AHP and hybrid Apriori-Gentic algorithm to mine causes of accident. First of all, the analytic hierarchy process (AHP) is used to sort the importance of the influencing factors about accident. On the basis of objective analysis, the influencing factors are quantified and the main influencing factors are selected. Then the genetic algorithm combined with Apriori is used to analyze the main influencing factors and find the expected association rules out. The experimental result shows that the model can improve the accuracy of mining and find more expected association rules. Finally the hybrid algorithm is parallelized to reduce time complexity, which makes the model has a good application potential.
