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Citation |
Kubota K, Nishimoto T, Ponte G. Trans. Soc. Automot. Eng. Jpn. 2021; 52(6): 1219-1226. |
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Copyright |
(Copyright © 2021, Society of Automotive Engineers of Japan) |
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DOI |
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PMID |
unavailable |
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Abstract |
In this study, an injury prediction algorithm for vehicle occupants was developed based on the South Australian Traffic Accident Reporting System (TARS), a traffic accident database. The algorithm uses information that can be identified from the accident location or reported by bystanders (e.g., speed limit distance from the centre of Adelaide in SA, etc) as risk factors. The best combination of factors for injury prediction was selected from 15 items based on the Akaike Information Criteria (AIC). The algorithm had an under-triage rate of less than 10% for the serious injured and an overtriage rate of 45.1% for the minor injured. This algorithm can contribute to the reduction of the number of fatalities by helping Automatic Crash Notification (ACN) systems to operate as Advanced Automatic Crash Notification (AACN) systems, or to be used by emergency medical services in on-scene triage. Language: ja |
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Keywords |
accident analysis; first aid; injury prediction; injury risk; macro data; micro data; occupant; safety |