Development of URGENCY 2.1 for the prediction of crash injury severity

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Bahouth, George T.¹; Digges, Kennerly H.¹; Bedewi, Nabih E.¹; Kuznetsov, Alexander¹; Augenstein, Jeffrey S.²; Perdeck, Elana²

Development of URGENCY 2.1 for the prediction of crash injury severity

Top Emerg Med. April–June 2004;26(2):157-165

©2004 Lippincott Williams & Wilkins, Inc.

Affiliations

¹FHWA/NHTSA National Crash Analysis Center, The George Washington University, Ashburn, Va

²The William Lehman Injury Research Center, University of Miami, Miami, Fla
Abstract and keywords

The URGENCY algorithm offers a means to determine the likelihood of serious injury in the event of a motor vehicle crash. The algorithm, based on historical crash data, relates crash characteristics to injury severity outcomes so that the most appropriate level of rescue care can be provided rapidly, accurately, and remotely in future systems. This article describes the development and enhancement of previous versions of the URGENCY algorithm to produce URGENCY 2.1. This algorithm allows for crash data, including crash characteristics, occupant characteristics, as well as vehicle factors useful for crash severity prediction. Recent enhancements of the URGENCY algorithm include an enhanced approach to the prediction of injury in the event of multiple impacts, improved criteria to classify crashes by mode, and the inclusion of additional data to train predictive models. Following enhancement, each algorithm has been tested for it's predictive value based on a sample of evaluation cases not used for model development. The results of this evaluation are presented.

Keywords: automatic crash notification (ACN); injury prediction; logistic regression
Links

Ovid: 00007815-200404000-00010

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Cited By (15)

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2008	Gabauer DJ, Gabler HC. Can delta-V be adjusted with structural and occupant restraint performance to improve prediction of chest acceleration? In: 52nd Annual Proceedings, Association for the Advancement of Automotive Medicine (AAAM). October 6-8, 2008; San Diego, CA.165-163.
2010	Kusano KD, Gabler HC. Potential occupant injury reduction in pre-crash system equipped vehicles in the striking vehicle of rear-end crashes. In: 54th Annual Proceedings, Association for the Advancement of Automotive Medicine (AAAM). October 17-20, 2010; Las Vegas, NV.203-213.
2012	Stigson H, Kullgren A, Rosén E. Injury risk functions in frontal impacts using data from crash pulse recorders. In: 56th Annual Proceedings, Association for the Advancement of Automotive Medicine (AAAM). October 14-17, 2012; Seattle, WA.267-276.
2011	Kusano KD, Gabler HC. Potential effectiveness of integrated forward collision warning, pre-collision brake assist, and automated pre-collision braking systems in real-world, rear-end collisions. In: Proceedings of the 22nd International Technical Conference on the Enhanced Safety of Vehicles (ESV). June 13-16, 2011; Washington, DC.
2011	Johnson N, Gabler HC. Evaluation of WinSmash accuracy in NHTSA side crash test reconstructions. In: Proceedings of the 22nd International Technical Conference on the Enhanced Safety of Vehicles (ESV). June 13-16, 2011; Washington, DC.
2015	Stitzel J, Weaver A, Schoell S, Barnard R, Talton J, Martin RS, Meredith JW. Occupant transportation decision algorithm: an advanced automatic crash notification algorithm developed and evaluated for motor vehicle crash occupant triage. In: Proceedings of the 24th International Technical Conference on the Enhanced Safety of Vehicles (ESV). June 8-11, 2015; Gothenburg, Sweden.
2015	Kiuchi T, Motomura Y, Matsumoto H, Mashiko K. Pilot study on advanced automatic collision notification and helicopter emergency medical service system in Japan. In: Proceedings of the 24th International Technical Conference on the Enhanced Safety of Vehicles (ESV). June 8-11, 2015; Gothenburg, Sweden.
2017	Stitzel J, Weaver A, Talton J, Barnard R, Schoell S, Martin RS, Meredith JW. Estimated triage improvement and societal cost savings with nationwide implementation of an advanced automatic crash notification algorithm in the U.S. In: Proceedings of the 25th International Technical Conference on the Enhanced Safety of Vehicles (ESV). June 5-8, 2017; Detroit, MI.
2022	Ejima S, Goto T, Peng Z, Cunningham K, Wang S. Injury severity prediction using functional data analysis based on select vehicle category from NASS-CDS. In: Proceedings of the 2022 International IRCOBI Conference on the Biomechanics of Injury. September 14-16, 2022; Porto, Portugal.22-32.
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2008	Gabauer DJ. Predicting Occupant Injury With Vehicle-Based Injury Criteria in Roadside Crashes [PhD thesis]. Virginia Polytechnic Institute and State University; June 6, 2008.
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2013	Kusano KD. Methodology for Determining Crash and Injury Reduction from Emerging Crash Prevention Systems in the U.S. [PhD thesis]. Virginia Polytechnic Institute and State University; 2013.