A traffic accident morphology diagnostic model based on a rough set decision tree |
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| Authors: | Gang Tao Huansheng Song Jun Liu Jiao Zou |
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| Institution: | 1. School of Information Engineering, Chang’an University, Xi’an, People’s Republic of China;2. Anhui Keli Information Industry Co. Ltd., Hefei, People’s Republic of China;3. Key Lab of Urban ITS Technology Optimization and Integration, Ministry of Public Security, Hefei, People’s Republic of China;4. Anhui Keli Information Industry Co. Ltd., Hefei, People’s Republic of China |
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| Abstract: | ABSTRACTTo build a traffic safety feature model and to quantify accident influences caused by some traffic violation behaviors of drivers, an accident diagnostic decision-making model is established. For the purpose of diagnosing accident morphologies, rough set theory is applied and the influence of traffic factors of different accident morphologies is quantified through calculating the degree of attribute importance, selecting core traffic factors and adopting a C4.5 decision tree algorithm. In the paper, road traffic accident data from 2008 to 2013 in Anhui Province are used. Typical rules are selected, targeted strategy proposals are put forward, and then, a scientific and reasonable diagnostic basis is provided for the diagnosis of traffic safety risks and the prediction of potential traffic accidents. |
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| Keywords: | Traffic accidents rough set decision tree algorithm C4 5 accident morphology decision diagnosis |
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