Development of Hierarchical Safety Performance Functions for Urban Mid-blocks

Crash frequency and severity are influenced by a variety of variables that represent regional, site, crash, and driver-vehicle unit characteristics. In the traditional methods of crash prediction, all the variables are considered at a single level and the multilevel structure inherent in the crash data is ignored. Hierarchical modelling is a statistical technique that allows multilevel data structure to be properly specified and estimated. In the present study, a hierarchical modelling approach was used to estimate the crash frequency and severity of single and dual carriageway roads. Since the crash patterns of single and dual carriageways were found to be different, separate models were developed for these facilities. A two-level design was adopted for crash frequency prediction and four level design for crash severity prediction. The two levels in the crash frequency prediction are geographic region level and traffic site level. The additional levels in severity prediction are crash level and driver-vehicle unit level. The study indicates that hierarchical models performed better for crash frequency and severity prediction. Hierarchical models are strongly advocated for crash data that has correlated observations within groups.

• Record URL:
  • https://doi.org/10.1016/j.sbspro.2013.11.203
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1877042813045941
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770428
• Supplemental Notes:
  • © 2013 Meera J. Krishnan et al.
• Authors:
  • Krishnan, Meera J
  • Anjana, S
  • Anjaneyulu, M V L R
• Conference:
  • 2nd Conference of Transportation Research Group of India (2nd CTRG)
  • Location: Agra , India
  • Date: 2013-12-12 to 2013-12-15
• Publication Date: 2013-12-2

Language

• English

Media Info

• Media Type: Digital/other
• Features: References; Tables;
• Pagination: pp 1078-1087
• Serial:
  • Procedia - Social and Behavioral Sciences
  • Volume: 104
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0428
  • Serial URL: http://www.sciencedirect.com/science/journal/18770428/53
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Crash data; Crash rates; Crash risk forecasting; Crash severity; Divided highways; Highway safety; Traffic crashes; Urban areas
• Subject Areas: Highways; Safety and Human Factors; I83: Accidents and the Human Factor;

Filing Info

• Accession Number: 01504711
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 27 2014 9:40AM