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Article

Examining Influential Factors on Injury Severities of Vehicle Occupants Based on In-depth Traffic Accident Database

교통사고-인체상해 데이터베이스 기반 차량 탑승자 상해 중증도 영향요인 분석

Soyoung JUNG1*
정 소영1*
1Assistant Professor, School of Safety Engineering, Dongyang University
1동양대학교 북서울캠퍼스 안전공학부 조교수
* Corresponding Author
31 August 2018. pp. 274-288
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Abstract

This study aims to examine influential factors on injury severities, especially severe injury versus fatality, involved in traffic accidents using crash type, vehicle damage, victim’s medical records from in-depth traffic accident database. To complete the study aim, forward and backward formats for sequential logistic regression model and their areas under the receiver operation characteristic curves were compared to select the optimal model format. As a result, forward format outperformed backward format. In the forward format, pre-hospital emergency medical service (EMS) time was significant to increase the probability of fatality compared to that of severe injury by 540% if the EMS vehicle response time was longer than 24minutes. Driver carelessness as the main cause of traffic accident was also significant to increase the probability of fatality compared to that of severe injury by 380%. Based on the resultant findings, safety improvement strategies such as enhancing EMS vehicles, professionals, equipment for quick initial medical aid, and EMS priority signals could be recommended to reduce the pre-hospital EMS time. Implementing more rumble strips or supplemental rest areas would also be helpful to reduce driver’s carelessness during driving. In addition, legislation for banning handheld phone, the relevant campaign or driver education program would be supportive to reduce the probability of traffic accident fatality as one of the safety improvement strategies. Combining the resultant findings and additional accident location information with GIS spatial clustering, this study would contribute to decision making for prioritizing future improvement policy implementation.

Keywords
in-depth traffic accident database
injury severities
pre-hospital EMS
safety improvement strategy
sequential logistic regression

본 연구는 교통사고-인체상해 데이터베이스에 저장된 교통사고, 차량파손 자료 및 사고차량 탑승자의 심층 의료기록을 이용하여 사고 당시 탑승자가 중증 외상을 입었지만 생존하는 경우 대비 중증 외상 후 사망한 경우를 비교한 후, 탑승자의 생존 여부에 영향을 미치는 유의미한 요인들을 분석하였다. 이를 위하여 본 연구는 Sequential Logistic Regression모형의 두 포맷을 사용하여 외상 중증도를 예측하였고 Receiver Operation Characteristic 커브의 넓이를 비교하여 더 우수한 포맷을 선택하였다. 그 결과, Sequential Logistic Regression모형의 Forward 포맷이Backward 포맷보다 우수한 성능을 보였다. 특히, 병원 전 응급의료서비스 시간이 24분이 초과될 경우에 중증 외상 후 생존 대비 사망 확률을 약 540% 증가시키는 것으로 나타났다. 또한, 교통사고의 주 원인이 졸음이나 기기조작과 같은 운전자의 부주의일 경우, 중증 외상 후 생존 대비 사망 확률을 약 380% 증가시키는 것으로 나타났다. 이와 같은 결과를 근거로 본 연구는 응급의료 구급차의 개수, 응급의료 처치 인력 및 장비 증가, 긴급차량 우선신호 시스템 운영, 럼블스트립 혹은 고속도로 졸음쉼터 증설, 운전 중 스마트 폰 기기 제한 법제화 및 관련 캠페인, 운전자 교육과 같은 대안이 필요함을 제시하였다. 본 연구결과를 기반으로 사고장소의 위치정보를 추가적으로 통합하여 GIS공간 클러스터링으로 발전시킨다면 안전향상대책 시행의 우선순위 지역을 선정하는 근거로도 본 연구의 활용가치가 높을 것이다.

키워드
교통사고-인체상해 데이터베이스
외상 중증도
병원 전 응급의료서비스
안전향상대책
sequential logistic regression
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Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 36
  • No :4
  • Pages :274-288
  • Received Date : 2018-05-25
  • Revised Date : 2018-07-18
  • Accepted Date : 2018-08-27
  • DOI :https://doi.org/10.7470/jkst.2018.36.4.274
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