Investigating the Impact of Lack of Motorcycle Annual Average Daily Traffic Data in Crash Modeling and the Estimation of Crash Modification Factors

The development of safety performance functions (SPFs) and crash modification factors (CMFs) requires data on traffic exposure. The analysis of motorcycle crashes can be especially challenging in this regard because few jurisdictions collect motorcycle traffic volume data systematically. To address this challenge, the project team conducted several analyses to explore (1) how much predictive power for an SPF is lost when motorcycle volumes are unknown and how this lack of information may affect the development of CMFs for motorcycle crashes, and (2) alternative methods for deriving accurate predictions of motorcycle crashes or motorcycle volumes. The results of the analyses show that when motorcycle volumes are not known, using total average annual daily traffic (AADT) on its own is sufficient for developing SPFs and CMFs. The potential bias due to missing motorcycle-specific AADT is sufficiently negligible where it exists so as not to preclude SPF and CMF development. The project team also concluded that attempting to predict motorcycle volumes is not possible using typically available roadway and county-level data. Improvement could possibly be found in trip generation type modeling at a disaggregate scale, although given the success of SPF development using total AADT, such an effort may not be worthwhile. A more significant issue in developing motorcycle crash SPFs and CMFs is working with relatively rare crash types. In the analyses undertaken, SPFs could not be developed for all motorcycle crash types or site types. More evidently, in the estimation of CMFs using simulated data, the CMF value varied significantly between simulation runs due to the low frequency of motorcycle crashes. In terms of research gaps, a database is needed that includes implemented countermeasures expected to affect motorcycle crashes along with the location, date of treatment, and treatment description. This information would aid researchers in identifying treatments that are feasible for study. The report also identifies several research gaps related to analytical methods, related gaps, and data limitations.

• Record URL:
  http://www.fhwa.dot.gov/publications/research/safety/16054/16054.pdf
• 
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/35720
• Corporate Authors:

  VHB

  Vienna, VA  United States 

  Persaud Lyon, Incorporated

  Toronto, Ontario  Canada 

  Federal Highway Administration

  Turner-Fairbank Highway Research Center
  McLean, VA  United States  22101

  Federal Highway Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Lyon, Craig
  • Persaud, Bhagwant
  • Scopatz, Robert
  • Himes, Scott
  • Albee, Matt
  • Lee, Thanh
• Publication Date: 2016-10

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 150p

Subject/Index Terms

• TRT Terms: Annual average daily traffic; Crash data; Crash modification factors; Mathematical prediction; Motorcycle crashes; Motorcycles; Traffic data; Traffic volume
• Identifier Terms: Safety Performance Functions
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01616889
• Record Type: Publication
• Report/Paper Numbers: FHWA-HRT-16-054
• Contract Numbers: DTFH61-13-D-00001
• Files: NTL, TRIS, ATRI, USDOT
• Created Date: Nov 21 2016 1:26PM