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 develo** countries.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Banuls VA, Turoff M, Hiltz SR (2013) Collaborative scenario modeling in emergency management through cross-impact. Technol Forecast Soc Change 80(9):1756–1774
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
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):402–411
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 51:1–10
Deng FF, Chen Q, Liu JJ (2012) The school bus accident cause analysis and safety management system. Intell Dev Sci Technol Econ 6:127–129
Dov Z, ** L (2016) Testing the effects of safety climate and disruptive children behavior on school bus drivers performance: a multilevel model. Accid Anal Prev 95:116–124
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 86:29–39
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):874–889
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
Karimnezhad A, Moradi F (2015) Road accident data analysis using Bayesian networks. Transp Lett 9(1):12–19
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 167:232–247
Li J, Zhang KZ, Guo JZ, Jiang K (2012a) Reasons analyzing of school bus accidents in China. Proc Eng 45:841–846
Li SX, Hong QL, **ao JM, Li XT, Liu BW (2012b) Study on the safety of kindergartens and primary schools based on fault tree analysis. Chin J Public Secur 4:38–41
Li Y, Su G, Zhang X, Zhang S, Yuan H (2015) Analysis of school bus accidents in China. Nat Hazards 79(2):1–12
Li Q, Liao QJ, LI J, Fu PF (2016) The school bus safety management system based on internet of things. Electron Prod 1:25–26
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):2098–2120
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 114:75–91
Mbakwe AC, Saka AA, Keechoo C, Young-Jae L (2016) Alternative method of highway traffic safety analysis for develo** countries using delphi technique and Bayesian network. Accid Anal Prev 93:135–146
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 hierarchies
Nirupama N, Hafezi H (2014) A short communication on school bus accidents: a review and analysis. Nat Hazards 74(3):2305–2310
Nordgard DE, Sand K (2010) Application of Bayesian networks for risk analysis of MV air insulated switch operation. Reliab Eng Syst Saf 95:1358–1366
Otman B, Yuan XH (2007) Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory. Inf Fusion 8:379–386
State Council of People’s Republic of China (2012) School bus safety management regulations
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 9:169–174
Tian CQ, Yang BJ (2014) A D-S evidence theory based fuzzy trust model in file-sharing P2P networks. Peer-to-Peer Netw Appl 7:332–345
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 93:845–856
Wu JS, Xu SD, Zhou R, Qin YP (2016) Scenario analysis of mine water inrush hazard using Bayesian networks. Saf Sci 89:231–239
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 46:126–136
**nhua Net (2010) **nhua 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
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 413–417
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 41:336–341
Yin AS, Ye YJ, Ding DX (2016) The school bus accident analysis based on the analytic hierarchy process. J Hum Traffic Sci Technol 1:168–171
Zeng Q, Huang HL (2014) Bayesian spatial joint modeling of traffic crashes on an urban road network. Accid Anal Prev 67:105–112
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 50:1049–1055
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):975–977
Zhu Z, Peng B, **ong C, Zhang L (2016) Short-term traffic flow prediction with linear conditional Gaussian Bayesian network. J Adv Transp 50(6):1111–1123
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
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, Bei**g.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wu, J., Fang, W., Tong, X. et al. Bayesian analysis of school bus accidents: a case study of China. Nat Hazards 95, 463–483 (2019). https://doi.org/10.1007/s11069-018-3491-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-018-3491-9