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A traffic accident morphology diagnostic model based on a rough set decision tree

Author

Listed:
  • Gang Tao
  • Huansheng Song
  • Jun Liu
  • Jiao Zou
  • Yanxiang Chen

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.

Suggested Citation

  • Gang Tao & Huansheng Song & Jun Liu & Jiao Zou & Yanxiang Chen, 2016. "A traffic accident morphology diagnostic model based on a rough set decision tree," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(8), pages 751-758, November.
    • Handle: RePEc:taf:transp:v:39:y:2016:i:8:p:751-758
    DOI: 10.1080/03081060.2016.1231894
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    Cited by:

    1. Farbod Farhangi & Abolghasem Sadeghi-Niaraki & Seyed Vahid Razavi-Termeh & Soo-Mi Choi, 2021. "Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale," Sustainability, MDPI, vol. 13(18), pages 1-25, September.

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