Freeway Incident Frequency Analysis Based on CART Method

  • Xuecai Xu Huazhong University of Science and Technology & University of Hong Kong
  • Željko Šarić University of Zagreb
  • Ahmad Kouhpanejade University of Nevada, Las Vegas
DOI: https://doi.org/10.7307/ptt.v26i3.1308
Keywords: data mining, classification and regression tree, incident frequency, binary tree,

Abstract

Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.

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Published
2014-05-26
How to Cite
1.
Xu X, Šarić Željko, Kouhpanejade A. Freeway Incident Frequency Analysis Based on CART Method. Promet [Internet]. 2014May26 [cited 2024Nov.21];26(3):191-9. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/1308
Issue
Vol 26 No 3 (2014)
Section
Articles

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