A two-stage mining framework to explore key risk conditions on one-vehicle crash severity

Yu-Chiun Chiou*, Lawrence W. Lan, Wen Pin Chen

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

This paper proposes a two-stage mining framework to explore the key risk conditions that may have contributed to the one-vehicle crash severity in Taiwan's freeways. In the first stage, a genetic mining rule (GMR) model is developed, using a novel stepwise rule-mining algorithm, to identify the potential risk conditions that best elucidate the one-vehicle crash severity. In the second stage, a mixed logit model is estimated, using the antecedent part of the mined-rules as explanatory variables, to test the significance of the risk conditions. A total of 5563 one-vehicle crash cases (226 fatalities, 1593 injuries and 3744 property losses) occurred in Taiwan's freeways over 2003-2007 are analyzed. The GMR model has mined 29 rules for use. By incorporating these 29 mined-rules into a mixed logit model, we further identify one key safe condition and four key risk conditions leading to serious crashes (i.e.; fatalities and injuries). Each key risk condition is discussed and compared with its adjacent rules. Based on the findings, some countermeasures to rectify the freeway's serious one-vehicle crashes are proposed.

Original languageEnglish
Pages (from-to)405-415
Number of pages11
JournalAccident Analysis and Prevention
Volume50
DOIs
StatePublished - 1 Jan 2013

Keywords

  • Crash severity
  • Genetic mining rule
  • Mixed logit model
  • One-vehicle crashes
  • Stepwise rule-mining algorithm

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