Accident Prediction Models for Quantifying Safety Benefit of Winter Road Maintenance

This research presents a modeling approach to investigate the association of the accident frequency during a snow-storm event with road surface conditions, visibility and other influencing factors controlling for traffic exposure. This methodology can be applied to the evaluation of different maintenance strategies using safety as a performance measure. As part of this approach, this research introduces a road surface condition index (RSI), similar to the commonly used friction measure, and uses this index as a representation of different road surface conditions. After the combination of different data sources, three event-based models including the Negative Binomial model (NB), the generalized NB model (GNB) and the zero inflated NB model (ZINB) are developed and compared for their capability to explain differences in accident frequency between individual snow storms. It was found that the GNB model best fits the data, and is most capable of capturing heterogeneity other than excess zeros. Among the main results, it was found that the RSI was statistically significant influencing the accident occurrence. According to our proposed index, our results suggest that a 10% improvement in road surface condition would lead to nearly an 11% reduction in the expected number of accidents. This research is the first showing the empirical relationship between safety and road surface conditions at a disaggregate level (event-based), making it feasible to quantify the safety benefits of alternative maintenance goals and methods.


  • English

Media Info

  • Media Type: DVD
  • Features: Figures; Maps; References; Tables;
  • Pagination: 23p
  • Monograph Title: TRB 89th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

Filing Info

  • Accession Number: 01152198
  • Record Type: Publication
  • Report/Paper Numbers: 10-0774
  • Files: TRIS, TRB
  • Created Date: Jan 25 2010 10:22AM