One of the primary objectives of the National Highway Traffic Safety Administration (NHTSA) is to reduce the staggering human toll and property damage that motor vehicle traffic crashes impose on our society. Crashes each year result in thousands of lives lost, hundreds of thousands of injured victims, and billions of dollars in property damage. Accurate data are required to support the development, implementation, and assessment of highway safety programs aimed at reducing this toll. NHTSA uses data from many sources, including the National Automotive Sampling System (NASS) General Estimates System (GES) which began operation in 1988. Providing data about all types of crashes involving all types of vehicles, the NASS GES is used to identify highway safety problem areas, provide a basis for regulatory and consumer information initiatives, and form the basis for cost and benefit analyses of highway safety initiatives. The NASS GES obtains its data from a nationally representative probability sample selected from the more than five million police-reported crashes which occur annually. These crashes include those that result in a fatality or injury and those involving major property damage. Although various sources suggest that there are many more crashes that are not reported to the police, the majority of these unreported crashes involve only minor property damage and no significant personal injury. By restricting attention to police-reported crashes, the NASS GES concentrates on those crashes of greatest concern to the highway safety community and the general public. This multi-year analytical user’s manual provides documentation on the historical coding practices of the General Estimates System from 1988 to 2015. In other words, this manual presents the evolution of NASS GES coding from inception through present. The manual includes the data elements that are contained in the NASS GES and other useful information that will enable the users to become familiar with the data system. FARS/NASS GES Coding and Validation Manuals provide more detailed definitions for each data element and attribute for a given year. (Author/publisher)
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