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Analysis and testing of Koornstra-type induced exposure models

File Language:
English


Details

  • Creators:
    Mengert, Peter
  • Corporate Creators:
    John A. Volpe National Transportation Systems Center (U.S.)
  • Subject/TRT Terms:
  • Publication/ Report Number:
    FR-45-U-FAA-86-01
  • Resource Type:
    Tech Report
  • Geographical Coverage:
    United States
  • Corporate Publisher:
    United States. Department of Transportation. National Highway Traffic Safety Administration
  • Abstract:
    Induced exposure models postulate a structure for accident data which permits the ; estimation of two factors: exposure and proneness. Since information on exposure ; is needed in order to assess the accident risk of different driver, vehicle, and ; environmental situations and since reliable exposure data is expensive to collect, ; induced exposure models hold the promise of a rich exposure data source which is ; perfectly matched to the accident data to be analyzed. This paper assesses the ; validity of the postulated structure of one induced exposure model: the Koornstra ; Model. The Koornstra Model was chosen for analysis and testing because it ; appeared to have the most potential for usefulness based on : ; a) previously reported favorable results (in limited testing); ; b) universal applicability; ; c) damaging criticism of certain other models; and ; d) a rich and well posed model structure. ; This paper analyses and tests the Koornstra Model from three different points of ; view: ; 1) Is it based on reasonable assumptions? ; 2) Does the model provide a significantly better fit to accident data than ; a simpler model which does not permit exposure or proneness to be ; estimated? ; 3) How do the exposure estimates provided by the model compare with ; those from externally collected data?
  • Format:
    PDF
  • Download URL:
    https://rosap.ntl.bts.gov/view/dot/5837/dot_5837_DS1.pdf
  • File Type:
    Filetype[PDF - 1.91 MB]
  • Collection(s):
    Volpe Center
  • Main Document Checksum:
    urn:sha-512:26639e3e954c940d19a8b9514568d3261267950e32f3361c55e1d75ef1be970978cfe89c717afcd0b8cfe7d03fc0b0e12fe466c7f52107291096d94d4bf8b1b9
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