title: A review of the validity of the underlying assumptions of quasi-induced exposure.
abstract: In recent years, the quasi-induced exposure technique has been widely implemented in a variety of traffic safety-related settings. One of the primary concerns associated with the applications of quasi-induced exposure is that the underlying assumptions are not explicitly verified or validated before the exposure measurement is adopted. Of principal interest is the assumption that the non-responsible driver/vehicles in two-vehicle crashes are representative of the general driving population on the road at the time of crash occurrence. The objective here is to provide an alternative to test the not-at-fault assumptions with the use of three-or-more-vehicle crashes, which are readily available in many crash databases. With the use of Michigan and Maine crash data as examples, the examination of the validity is developed at two levels: (1) at all locations where crashes took place; (2) at locations where three-or-more-vehicle crashes are prone to occur. Non-responsible drivers are disaggregated by three basic driver-vehicle characteristics (age, gender, and vehicle type) and compared at these two levels for statistical and operational (practical) differences. Examination of the results demonstrates that all of the examined non-responsible driver distributions are consistently similar from both operational and statistical points of view. Compared to other approaches to validation, such as using "exposure truth" (e.g., actual vehicle miles traveled or a trip diary), the proposed validation is much more simplistic, straightforward, and cost-effective.
authors.affiliation: College of Traffic and Transportation, Southwest Jiaotong University, Chengdu, China. jiangxi1@msu.edu
authors.forename: Xinguo
authors.initials: X
authors.lastname: Jiang
authors.forename: Richard W
authors.initials: RW
authors.lastname: Lyles
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001334:Automobile Driving / Q000706:statistics & numerical data*; D015984:Causality; D005260:Female; D006801:Humans; D016365:Liability, Legal; D008281:Maine; D008297:Male; D008824:Michigan; D008875:Middle Aged; D018986:Motor Vehicles; D016011:Normal Distribution; D015203:Reproducibility of Results; D012737:Sex Factors; D055815:Young Adult