Methodology for fitting and updating predictive accident models with trend

Reliable predictive accident models (PAMs) (also referred to as Safety Performance Functions (SPFs)) have a variety of important uses in traffic safety research and practice. They are used to help identify sites in need of remedial treatment, in the design of transport schemes to assess safety implications, and to estimate the effectiveness of remedial treatments. The PAMs currently in use in the UK are now quite old; the data used in their development was gathered up to 30 years ago. Many changes have occurred over that period in road and vehicle design, in road safety campaigns and legislation, and the national accident rate has fallen substantially. It seems unlikely that these ageing models can be relied upon to provide accurate and reliable predictions of accident frequencies on the roads today. This paper addresses a number of methodological issues that arise in seeking practical and efficient ways to update PAMs, whether by re-calibration or by re-fitting. Models for accidents on rural single carriageway roads have been chosen to illustrate these issues, including the choice of distributional assumption for overdispersion, the choice of goodness of fit measures, questions of independence between observations in different years, and between links on the same scheme, the estimation of trends in the models, the uncertainty of predictions, as well as considerations about the most efficient and convenient ways to fit the required models.

• Record URL:
  • https://doi.org/10.1016/j.aap.2013.03.009
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457513001012
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • Abstract reprinted with permission from Elsevier.
• Authors:
  • Connors, Richard D
  • Maher, Mike
  • Wood, Alan
  • Mountain, Linda
  • Ropkins, Karl
• Publication Date: 2013-7

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 82-94
• Serial:
  • Accident Analysis & Prevention
  • Volume: 56
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Crash rates; Crash risk forecasting; Goodness of fit; Mathematical prediction; Methodology; Traffic crashes; Traffic lanes; Traffic safety; Trend (Statistics); Uncertainty
• Identifier Terms: Safety Performance Functions
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors; I80: Accident Studies;

Filing Info

• Accession Number: 01484042
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 17 2013 2:16PM