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Sampling Issues In Rear-End Pre-Crash Data Collection
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The involvement of a driver in a rear-end crash and the manner in which his/her vehicle collides with other vehicle(s) depends not only on his/her perception of the complex scenario that emerges prior to the crash, but also on the pre-crash driving behavior, response to the imminent crash situation and performance in resolving the driving conflicts. Obviously, any effort directed towards crash countermeasures must start from data collection that can provide information about the driver-related parameters. This would further mean deploying vehicles on the roadways, which are equipped with certain recording devices, as well as making the voluntary drivers available. Due to the random nature of these crashes, the number of vehicles required to deploy for observing a certain number of them involved in such crashes may be large. On the other hand, due to budgetary restrictions and operational constraints, the sample size actually required may not be permissible. The effort should be, therefore, to make the best use of the available resources.

The objective of this study is to propose sampling strategies by which the maximum amount of information, both in terms of the number of rear-end crashes and coverage of the target population, could be obtained by deploying the minimum number of vehicles. Toward that end, sample stratification criteria are proposed. In addition, two estimates are proposed: (i) the number of vehicles that would be involved in rear-end crashes out of a certain number deployed, and (ii) the least number of vehicles that would be required for observing a specific number of vehicles involved in rear-end crashes. These estimates are required for implementation of the approach proposed in this study to resolve the sampling issues.

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