Validation of Injury Prediction Model of Japan and URGENCY of United States

日米傷害予測モデルの交通事故実態調査に基づく検証

Advanced Automatic Collision Notification (AACN) is a notification system that uses data about previous accidents to predict the extent of passenger injuries. The purpose of this study is to compare injury prediction models: the Honda-Nihon University model of Japan and the URGENCY model of the United States. These models are verified using 116 cases of Japanese in-depth accident data collected by the Nihon University and Chiba Hokusoh Hospital of the Nippon Medical School. Receiver operating characteristic (ROC) curves were generated by evaluating serious injury risks from the in-depth accident data, and the best models were selected to maximize the area under the curve (AUC) of ROC. In this result, the full model of the Honda-Nihon University model was the best model for predicting injuries during accidents in Japan, (AUC = 0.7699).交通事故と同時に自動車乗員の重傷率を予測することにより，救命率を向上させる研究が日本や米国で行われている．本研究では日本と米国で報告された傷害予測モデルを日本大学と日本医科大学で2009年より調査している交通事故ミクロデータ約150件に照合し検証した．

• Record URL:
  https://doi.org/10.11351/jsaeronbun.46.127
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/02878321
• Authors:
  • Mukaigawa, Kosuke
  • Nishimoto, Tetsuya
  • Tominaga, Shigeru
  • Motomura, Tomokazu
  • Mashiko, Kunihiro
• Publication Date: 2015

Language

• English
• Japanese

Media Info

• Media Type: Web
• Features: Figures; Photos; References; Tables;
• Pagination: pp 127-132
• Serial:
  • Transactions of Society of Automotive Engineers of Japan
  • Volume: 46
  • Issue Number: 1
  • Publisher: Society of Automotive Engineers of Japan
  • ISSN: 0287-8321
  • EISSN: 1883-0811
  • Serial URL: https://www.jstage.jst.go.jp/browse/jsaeronbun
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Crash data; Highway safety; Injuries; Mathematical prediction; Safety; Validation
• Identifier Terms: Advanced Automatic Collision Notification; URGENCY algorithm
• Geographic Terms: Japan; United States
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01665027
• Record Type: Publication
• Source Agency: Japan Science and Technology Agency (JST)
• Files: TRIS, JSTAGE
• Created Date: Mar 30 2018 4:32PM