Abstract
In this paper, we aim to identify the significant variables that contribute to the injury severity level of the person in the car when an accident happens and build a statistical model for predicting the maximum injury severity level as well as estimating the potential economic cost in a car accident based on those variables. The General Estimates System data, which is a representative sample of police-reported motor vehicle crashes of all types collected by the National Highway Transportation Safety Administration, from the years 2012 to 2013 is the main data source. Some other data sources such as the car safety rating from the United State Department of Transformation and the state-specific cost of crash deaths fact sheets are also used in the predictive model building process. An interactive system programmed in HyperText Markup Language, Cascading Style Sheets and JavaScript is developed based on the results of predictive modeling. The system is hosted on a website at http://gessmu.azurewebsites.net for public access. The system allows users to input variables that are significant contributors in car accidents and obtain the predicted maximum injury severity level and potential economic cost of a car accident.
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Acknowledgements
This paper is based on an entry of the American Statistical Association Government Statistics Section 2016 Data Challenge. The authors would like to thank the guest editors, Dr. Roya Amjadi and Dr. Wendy Martinez, for providing them the opportunity to contribute to this special issue, and to thank the two anonymous referees for their useful comments and suggestions on an earlier version of this manuscript which resulted in this improved version. H. K. T. Ngs work was supported by a grant from the Simons Foundation (#709773 to Tony Ng).
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Alkan, G., Farrow, R., Liu, H. et al. Predictive modeling of maximum injury severity and potential economic cost in a car accident based on the General Estimates System data. Comput Stat 36, 1561–1575 (2021). https://doi.org/10.1007/s00180-021-01074-7
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DOI: https://doi.org/10.1007/s00180-021-01074-7