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Natural Hazards

, Volume 95, Issue 3, pp 463–483 | Cite as

Bayesian analysis of school bus accidents: a case study of China

  • Jiansong WuEmail author
  • Weipeng Fang
  • Xing Tong
  • Shuaiqi Yuan
  • Weiqi Guo
Original Paper
  • 185 Downloads

Abstract

Prevention and control of school bus accidents have been a hot spot topic around the world. The catastrophic accident can result in severe casualties associated with negative social impacts. In this study, a Bayesian network (BN) model is established for school bus accident assessments considering the influential factors including human error, vehicle failure, environmental impacts and management deficiency. The conditional probabilities of a few root nodes are statistically obtained based on school bus accidents that occurred in the past decade in China. The conditional probabilities of other BN nodes are determined by expert knowledge with treatment by the Dempster–Shafer evidence theory. The consequences of different scenarios of school accidents are estimated via changing the state values of some BN nodes. Furthermore, by conducting sensitivity analysis to the proposed BN, it is identified that “overload” is the most influential factor causing a school accident. The results of the proposed model indicate that the integration of Bayesian network and the Dempster–Shafer evidence theory is an effective framework for school bus accident assessment, which could provide more practical analysis for school bus accidents. This study could contribute to providing technical supports for school bus safety particularly in developing countries.

Keywords

School bus accident Overload Human error Vehicle failure Bayesian network 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 11502283) and the Yue Qi Scholar/Young Scholar Program of China University of Mining & Technology, Beijing.

References

  1. Banuls VA, Turoff M, Hiltz SR (2013) Collaborative scenario modeling in emergency management through cross-impact. Technol Forecast Soc Change 80(9):1756–1774CrossRefGoogle Scholar
  2. China News Net (2010) National news. The circular from China‘s Safety committee office of the state council about a school bus accident which fell in a river. Available at http://www.chinanews.com/gn/2010/12-31/2760137.shtml
  3. de Oña J, Mujalli RO, Calvo FJ (2011) Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. Accid Anal Prev 43(1):402–411CrossRefGoogle Scholar
  4. de Oña J, López G, Mujalli R, Calvo FJ (2013) Analysis of traffic accidents on rural highways using latent class clustering and Bayesian networks. Accid Anal Prev 51:1–10CrossRefGoogle Scholar
  5. Deng FF, Chen Q, Liu JJ (2012) The school bus accident cause analysis and safety management system. Intell Dev Sci Technol Econ 6:127–129Google Scholar
  6. Dov Z, Jin L (2016) Testing the effects of safety climate and disruptive children behavior on school bus drivers performance: a multilevel model. Accid Anal Prev 95:116–124CrossRefGoogle Scholar
  7. Feng SM, Li ZN, Ci YS, Zhang GH (2016) Risk factors affecting fatal bus accident severity: their impact on different types of bus drivers. Accid Anal Prev 86:29–39CrossRefGoogle Scholar
  8. Golam K, Rehan S, Solomon T (2016) A fuzzy Bayesian belief network for safety assessment of oil and gas pipelines. Struct Infrastruct Eng 12(8):874–889CrossRefGoogle Scholar
  9. Hainan News Net (2014) mportant news. < topics in focus > Focus on Hainan student rollover accident in a spring outing. Available at http://news.hainan.net/hainan/yaowen/1/2014/04/12/1836995.shtml
  10. Karimnezhad A, Moradi F (2015) Road accident data analysis using Bayesian networks. Transp Lett 9(1):12–19CrossRefGoogle Scholar
  11. Khakzad N, Landucci G, Reniers G (2017) Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects. Reliab Eng Syst Saf 167:232–247CrossRefGoogle Scholar
  12. Li J, Zhang KZ, Guo JZ, Jiang K (2012a) Reasons analyzing of school bus accidents in China. Proc Eng 45:841–846CrossRefGoogle Scholar
  13. Li SX, Hong QL, Xiao JM, Li XT, Liu BW (2012b) Study on the safety of kindergartens and primary schools based on fault tree analysis. Chin J Public Secur 4:38–41Google Scholar
  14. Li Y, Su G, Zhang X, Zhang S, Yuan H (2015) Analysis of school bus accidents in China. Nat Hazards 79(2):1–12Google Scholar
  15. Li Q, Liao QJ, LI J, Fu PF (2016) The school bus safety management system based on internet of things. Electron Prod 1:25–26Google Scholar
  16. Marcelo RM, Adriana MS, Enrique LD (2014) A methodology for risk analysis based on hybrid Bayesian networks: application to the regasification system of liquefied natural gas onboard a floating storage and regasification unit. Risk Anal 34(12):2098–2120CrossRefGoogle Scholar
  17. Matellini DB, Wall AD, Jenkinson ID, Wang J, Pritchard R (2013) Modelling dwelling fire development and occupancy escape using Bayesian network. Reliab Eng Syst Saf 114:75–91CrossRefGoogle Scholar
  18. Mbakwe AC, Saka AA, Keechoo C, Young-Jae L (2016) Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network. Accid Anal Prev 93:135–146CrossRefGoogle Scholar
  19. Ministry of Public Security of People’s Republic of China (2003) The ministry of public security on revising for the announcement of standard of road traffic accident hierarchiesGoogle Scholar
  20. Nirupama N, Hafezi H (2014) A short communication on school bus accidents: a review and analysis. Nat Hazards 74(3):2305–2310CrossRefGoogle Scholar
  21. Nordgard DE, Sand K (2010) Application of Bayesian networks for risk analysis of MV air insulated switch operation. Reliab Eng Syst Saf 95:1358–1366CrossRefGoogle Scholar
  22. Otman B, Yuan XH (2007) Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory. Inf Fusion 8:379–386CrossRefGoogle Scholar
  23. State Council of People’s Republic of China (2012) School bus safety management regulationsGoogle Scholar
  24. Sun DP, Wang K, Chen GM, Liu PX (2016) Application of Bayesian network in successful rate analysis of emergency evacuation of offshore platform. Chin J Saf Sci 9:169–174Google Scholar
  25. Tian CQ, Yang BJ (2014) A D-S evidence theory based fuzzy trust model in file-sharing P2P networks. Peer-to-Peer Netw Appl 7:332–345CrossRefGoogle Scholar
  26. Trucco P, Cagno E, Ruggeri F, Grande O (2008) A Bayesian belief network modeling of organizational factors in risk analysis: a case study in maritime transportation. Reliab Eng Syst Saf 93:845–856CrossRefGoogle Scholar
  27. Wu JS, Xu SD, Zhou R, Qin YP (2016) Scenario analysis of mine water inrush hazard using Bayesian networks. Saf Sci 89:231–239CrossRefGoogle Scholar
  28. Wu JS, Zhou R, Xu SD, Wu ZW (2017) Probabilistic analysis of natural gas pipeline network accident based on Bayesian network. J Loss Prev Process Ind 46:126–136CrossRefGoogle Scholar
  29. Xinhua Net (2010) Xinhua education. Kindergarten school bus rushed into the pool results 2 killed and 13 wounded. Available at http://education.news.cn/2010-12/08/c_12860888_3.htm
  30. Xu HG, Zhang HY, Zong F (2010) Bayesian network-based road traffic accident causality analysis. In: WASE international conference on information engineering (ICIE), Beidaihe, Hebei, China vol 3, pp 413–417Google Scholar
  31. Yang JZ, Corinne P, Cheng G, Erin H, Scott F, Marizen R (2009) Incidence and characteristics of school bus crashes and injuries. Accid Anal Prev 41:336–341CrossRefGoogle Scholar
  32. Yin AS, Ye YJ, Ding DX (2016) The school bus accident analysis based on the analytic hierarchy process. J Hum Traffic Sci Technol 1:168–171Google Scholar
  33. Zeng Q, Huang HL (2014) Bayesian spatial joint modeling of traffic crashes on an urban road network. Accid Anal Prev 67:105–112CrossRefGoogle Scholar
  34. Zhao LJ, Wang XL, Qian Y (2012) Analysis of factors that influence hazardous material transportation accidents based on Bayesian networks: a case study in China. Saf Sci 50:1049–1055CrossRefGoogle Scholar
  35. Zheng Z, Qi S, Xu Y (2013) A new type of human-made disaster from the frequent school bus accidents in China. Nat Hazards 67(2):975–977CrossRefGoogle Scholar
  36. Zhu Z, Peng B, Xiong C, Zhang L (2016) Short-term traffic flow prediction with linear conditional Gaussian Bayesian network. J Adv Transp 50(6):1111–1123CrossRefGoogle Scholar
  37. Zou X, Yue WL (2017) A Bayesian network approach to causation analysis of road accidents using Netica. J Adv Transp.  https://doi.org/10.1155/2017/2525481 Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Jiansong Wu
    • 1
    Email author
  • Weipeng Fang
    • 1
  • Xing Tong
    • 1
  • Shuaiqi Yuan
    • 1
  • Weiqi Guo
    • 1
  1. 1.Department of Safety Technology and ManagementChina University of Mining & TechnologyBeijingChina

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