A traffic accident morphology diagnostic model based on a rough set decision tree. Issue 8 (16th November 2016)

• Record Type: Journal Article
• Authors: Tao, Gang; Song, Huansheng; Liu, Jun; Zou, Jiao; Chen, Yanxiang
• Is Part Of: Transportation planning and technology. Volume 39:Issue 8(2016)
• Page Start: 751
• Page End: 758
• Publication Date: 2016-11-16
• Subjects: Traffic accidents -- rough set -- decision tree algorithm -- C4.5 -- accident morphology -- decision diagnosis; Transportation -- Periodicals; Transportation -- Research -- Periodicals; Local transit -- Periodicals; Transportation and state -- Periodicals; 388
• DOI: 10.1080/03081060.2016.1231894
• Languages: English
• ISSNs: 0308-1060

Abstract

ABSTRACT: To 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.