Sampling Design for National Motor Vehicle Crash Causation Survey.

Author(s)
Choi, E. Zhang, F. Noh, E. Singh, S. & Chen, C.
Year
Abstract

The National Motor Vehicle Crash Causation Survey (NMVCCS) is a survey that collects information on the vehicle, the roadway, and the environmental conditions, as well as the behavioral aspect of the human from crashes involving light vehicles. The primary focus of the survey are the events immediately prior to a crash as well as the associated factors, as described by the occupants and witnesses, reported by the police, and assessed by the NMVCCS researchers. One of the special features of NMVCCS is collecting information at the crash scene itself, thus enabling the researcher to obtain first-hand information while it is still fresh. Due to the nature of the targeted information and the operation method, this survey requires a complex sample design to get national representation at a reasonable cost. Both time and location of a crash are considered in the selection of crashes to attain high efficiency of the on-scene survey. A two-dimensional sampling frame reflecting on both space and time of crash occurrence is used in sampling crashes from among those occurring between 6 a.m. and midnight. A probability-based sampling procedure is performed in multiple stages. In order to make the NMVCCS sample a nationally representative data, the inclusion probability in each sampling stage is taken into account in developing the weights. While doing so, appropriate consideration is given to the adjustments made for the missing crashes from which information could not be collected due to the operational difficulties and other issues. Some national estimates and their standard errors, based on the data collected during the first year of its operation, are presented.

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Publication

Library number
C 44213 (In: C 43862 CD-ROM) /80 / ITRD E842035
Source

In: Compendium of papers CD-ROM 87th Annual Meeting of the Transportation Research Board TRB, Washington, D.C., January 13-17, 2008, 16 p.

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