Bootstrap resampling approach to disaggregate analysis of road crashes in Hong Kong

Xin Pei, Nang Ngai Sze, S. C. Wong, Danya Yao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

30 Citations (Scopus)


As the relationships between crashes, crash severity, and possible risk factors can vary depending on the type of collision, we attempt to develop separate prediction models for different crash types (i.e., single- versus multi-vehicle crashes and slight injury versus killed and serious injury crashes). Taking advantage of the availability of crash and traffic data disaggregated by time and space, it is possible to identify the factors that may contribute to crash risks in Hong Kong, including traffic flow, road design, and weather conditions. To remove the effects of excess zeros on prediction performance in a highly disaggregated crash prediction model, a bootstrap resampling method is applied. The results indicate that more accurate and reliable parameter estimates, with reduced standard errors, can be obtained with the use of a bootstrap resampling method. Results revealed that factors including rainfall, geometric design, traffic control, and temporal variations all determined the crash risk and crash severity. This helps to shed light on the development of remedial engineering and traffic management and control measures.
Original languageEnglish
Pages (from-to)512-520
Number of pages9
JournalAccident Analysis and Prevention
Publication statusPublished - 1 Oct 2016
Externally publishedYes


  • Bootstrap resampling method
  • Count data model
  • Injury severity
  • Multiple-vehicle crash
  • Single-vehicle crash

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Safety, Risk, Reliability and Quality
  • Public Health, Environmental and Occupational Health
  • Law


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