Journal of Auto-vehicle Safety Association (자동차안전학회지)

Korean Auto-vehicle Safety Association (한국자동차안전학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Analysis of Representative Bus Crash Types through Establishment of Bus In-depth Accident Data

버스 실사고 데이터 구축을 통한 대표 버스충돌유형 분석 연구

  • 김형준 (아주대학교 교통시스템공학과) ;
  • 장정아 (아주대학교 TOD 기반 지속가능 도시교통 연구센터) ;
  • 이인식 (아주대학교 교통시스템공학과) ;
  • 이용주 (아주대학교 TOD 기반 지속가능 도시교통 연구센터) ;
  • 오세창 (아주대학교 교통시스템공학과)
  • Received : 2020.10.28
  • Accepted : 2020.11.17
  • Published : 2020.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, crash situations of representative bus crash types were elicited by analyzing a total of 1,416 bus repair record which were collected in 2018~2019. K-means clustering was used as a methodology for this study. Bus repair record contain the information of repair term, type of bus operation, responsibility of accident, weather condition, road surface condition, type of accident, other party, type of road and type of location for each data. Also, by checking collision parts of each bus repair record, each record was classified by types of collision regions. From this, 760 record are classified to frontal type, 363 record are classified to middle-frontal type, 374 record are classified to middle-rear type and 331 record are classified to rear type. As mentioned, k-means clustering was performed on each type of collision parts. As a result, this study analyzed the severity of bus crash based on actual bus accident data which are based on bus repair record not the crash data from the TAAS. Also, this study presented crash situation of representative bus crash types. It is expected that this study can be expanded to analyzing hydrogen bus crash and defining indicators of hydrogen bus safety.

Keywords

Acknowledgement

본 연구는 국토교통부 수소버스 안전성 평가기술 및 장비개발 사업의 연구비 지원(과제번호 20HBST-B158067-01)에 의해 수행되었습니다.

References

  1. MacQueen, J., 1967, "Some methods for classification and analysis of multivariate observations", In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1, No. 14, pp. 281~297.
  2. 이문석, 정건희, 2018, "K-평균 알고리즘을 이용한 적설관측소 군집분석", 한국방재학회 학술대회논문집, Vol. 18, No. 2, pp. 367~367.
  3. Anderson, T. K., 2009, "Kernel density estimation and K-means clustering to profile road accident hotspots", Accident Analysis & Prevention, Vol. 41, pp. 359~364. https://doi.org/10.1016/j.aap.2008.12.014
  4. Kaplan, S. and Prato, C. G., 2012, "Risk factors associated with bus accident severity in the united states: A generalized ordered logit model", Journal of safety research, Vol. 43, No. 3, pp. 171~180. https://doi.org/10.1016/j.jsr.2012.05.003
  5. Chu, H. C., 2013, "Assessing factors causing severe injuries in crashes of high-deck buses in long-distance driving on freeways", Accident Analysis & Prevention, Vol. 62, pp. 130~136. https://doi.org/10.1016/j.aap.2013.09.016
  6. Prato, C. G. and Kaplan, S., 2014, "Bus accident severity and passenger injury: Evidence from Denmark, Eur. Transp. Res., Rev 6, pp. 17~30. https://doi.org/10.1007/s12544-013-0107-z
  7. Feng, S., Li, Z., Ci, Y. and Zhang, G., 2016, "Risk factors affecting fatal bus accident severity: Their impact on different types of bus drivers", Accident Analysis & Prevention, Vol. 86, pp. 29~39. https://doi.org/10.1016/j.aap.2015.09.025
  8. 빈미영, 손슬기, 2018, "버스운전자 성별 교통사고심각도 영향요인 분석", 대한교통학회지, Vol. 36, pp. 440~451. https://doi.org/10.7470/jkst.2018.36.6.440
  9. Sam, E. F., Daniels, S., Brijs, K., Brijs, T. and Wets, G., 2018, "Modelling public bus/minibus transport accident severity in Ghana", Accident Analysis & Prevention, Vol. 119, pp. 114~121. https://doi.org/10.1016/j.aap.2018.07.008
  10. 윤상원, 고승영, 김동규, 2018, "순서형 프로빗 모형을 이용한 버스 운송사업 유형별 사고심각도 영향요인 분석", 대한교통학회지, Vol. 36, No. 1, pp. 13~22. https://doi.org/10.7470/jkst.2018.36.1.013