Sampling issues in rear-end pre-crash data collection.

Auteur(s)
Singh, S.
Jaar
Samenvatting

A common type of crash that occurs on the roadways is the rear-end crash. Every year a large number of drivers are involved in such crashes. In order to develop effective crash countermeasures, it is important to have a better understanding of the driving behaviour and performance of a driver prior to a rear-end crash. For that purpose, experiments need to be conducted in which the drivers can be observed in "naturalistic" settings and data can be collected on the driver-related parameters. This study discusses some of the sampling issues involved in the process of data collection in the above context. Contingency analysis is conducted to suggest criteria for stratifying the target population. A probabilistic approach is used for allocating the sample over the strata thus formed. An estimate of the number of vehicles needed to observe a specific number of rear-end crashes is obtained. This estimation problem is treated as the "discrete waiting-time" problem. Additionally, binomial probability distribution is used to estimate the number of drivers who would be involved in rear-end crashes as a result of deploying a certain number of vehicles. This estimate can be used to assess the potential of a given sample for acquiring the desired amount of information. When compared with some of the other methods of allocation (equal and proportional), the sample allocation criterion proposed in this study suggested much smaller sample size. Due to the random nature of rear-end crashes, the number of vehicles actually required for observing a certain number of rear-end crashes is likely to be large, while a smaller number may be deployable due to budgetary restrictions or other operational constraints. The sampling strategies are proposed for resolving the issues that may arise in such situations. The approach adopted in this study is fairly general and can be used to resolve the sampling issues in similar setups. Two databases, the General Estimates System (GES) and the Fatality Analysis Reporting System (FARS), compiled by the National Highway Traffic Safety Administration (NHTSA), have been used in this study. (Author/publisher)

Publicatie

Bibliotheeknummer
20070710 ST [electronic version only]
Uitgave

Washington, D.C., U.S. Department of Transportation DOT, National Highway Traffic Safety Administration NHTSA, National Center for Statistics & Analysis NCSA, 2003, III + 23 p., 2 ref.; DOT HS 809 541

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