Bayesian hierarchical modelling of traffic barrier crash severity

References

• Aarts, L., & Van Schagen, I. (2006). Driving speed and the risk of road crashes: A review. Accident; Analysis and Prevention, 38(2), 215–224. https://doi.org/10.1016/j.aap.2005.07.004
   PubMed Web of Science ®Google Scholar
• Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422), 669–679. https://doi.org/10.1080/01621459.1993.10476321
   Web of Science ®Google Scholar
• Charlton, S. G., & De Pont, J. J. (2007). Curve speed management (No. 323).
   Google Scholar
• Chliaoutakis, J. E., Demakakos, P., Tzamalouka, G., Bakou, V., Koumaki, M., & Darviri, C. (2002). Aggressive behavior while driving as predictor of self-reported car crashes. Journal of Safety Research, 33(4), 431–443. https://doi.org/10.1016/S0022-4375(02)00053-1
   PubMed Web of Science ®Google Scholar
• Dahlen, E. R., Martin, R. C., Ragan, K., & Kuhlman, M. M. (2005). Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accident; Analysis and Prevention, 37(2), 341–348. https://doi.org/10.1016/j.aap.2004.10.006
   PubMed Web of Science ®Google Scholar
• Deffenbacher, J. L., Oetting, E. R., & Lynch, R. S. (1994). Development of a driving anger scale. Psychological Reports, 74(1), 83–91. https://doi.org/10.2466/pr0.1994.74.1.83
   PubMed Web of Science ®Google Scholar
• Donnell, E. T., & Mason, J. M. (2004). Predicting the severity of median-related crashes in pennsylvania by using logistic regression. Transportation Research Record: Journal of the Transportation Research Board, 1897(1), 55–63. https://doi.org/10.3141/1897-08
   Google Scholar
• Evans, L. (1996). Safety-belt effectiveness: The influence of crash severity and selective recruitment. Accident; Analysis and Prevention, 28(4), 423–433. https://doi.org/10.1016/0001-4575(96)00006-1
   PubMed Web of Science ®Google Scholar
• Facts, T. S. (2004). A compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system (p. 20590). National Highway Traffic Safety Administration.
   Google Scholar
• FARS & National Highway Traffic Safety Administration. (2016). Fatality analysis reporting system. Retrieved from http://www-Fars.Nhtsa.Dot.Gov/Main/Index.Aspx.
   Google Scholar
• FHWA (2014). Roadway departure crashes. Retrieved from https://safety.fhwa.dot.gov/local_rural/training/fhwasa14092/road_dep.pdf
   Google Scholar
• Gelman, A., & Meng, X. (2004). Applied Bayesian modeling and causal inference from incomplete-data perspectives. John Wiley & Sons.
   Google Scholar
• Gelman, A., Stern, H. S., Carlin, J. B., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. Chapman and Hall/CRC.
   Google Scholar
• Goldstein, H. (2011). Multilevel statistical models. John Wiley & Sons.
   Google Scholar
• Guide, R. D. (1996). AASHTO. Washington, DC.
   Google Scholar
• Hu, W., & Donnell, E. T. (2010). Median barrier crash severity: Some new insights. Accident; Analysis and Prevention, 42(6), 1697–1704. https://doi.org/10.1016/j.aap.2010.04.009
   PubMed Web of Science ®Google Scholar
• Huang, H., Chin, H. C., & Haque, M. M. (2008). Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis. Accident; Analysis and Prevention, 40(1), 45–54. https://doi.org/10.1016/j.aap.2007.04.002
   PubMed Web of Science ®Google Scholar
• Jama, H. H., Grzebieta, R. H., Friswell, R., & McIntosh, A. S. (2011). Characteristics of fatal motorcycle crashes into roadside safety barriers in Australia and New Zealand. Accident; Analysis and Prevention, 43(3), 652–660. https://doi.org/10.1016/j.aap.2010.10.008
   PubMed Web of Science ®Google Scholar
• Jones, A. P., & Jørgensen, S. H. (2003). The use of multilevel models for the prediction of road accident outcomes. Accident; Analysis and Prevention, 35(1), 59–69.
   PubMed Web of Science ®Google Scholar
• Khattak, A. J., Pawlovich, M. D., Souleyrette, R. R., & Hallmark, S. L. (2002). Factors related to more severe older driver traffic crash injuries. Journal of Transportation Engineering, 128(3), 243–249. https://doi.org/10.1061/(ASCE)0733-947X(2002)128:3(243)
   Web of Science ®Google Scholar
• Khattak, A., Kantor, P., & Council, F. (1998). Role of adverse weather in key crash types on limited-access: Roadways implications for advanced weather systems. Transportation Research Record: Journal of the Transportation Research Board, 1621(1), 10–19. https://doi.org/10.3141/1621-02
   Web of Science ®Google Scholar
• Kloeden, C. N., McLean, A. J., Moore, V. M., & Ponte, G. (1997). Travelling speed and the risk of crash involvement volume 2—Case and reconstruction details. NHMRC Road Accident Research Unit, the University of Adelaide.
   Google Scholar
• Kloeden, C. N., McLean, J., & Glonek, G. F. V. (2002). Reanalysis of travelling speed and the risk of crash involvement in Adelaide South Australia. Australian Transport Safety Bureau.
   Google Scholar
• Kockelman, K. M., & Kweon, Y. (2002). Driver injury severity: An application of ordered probit models. Accident; Analysis and Prevention, 34(3), 313–321. https://doi.org/10.1016/s0001-4575(01)00028-8
   PubMed Web of Science ®Google Scholar
• Mashhadi, M. M. R., Wulff, S. S., & Ksaibati, K. (2017). Utilizing crash and violation data to assess unsafe driving actions. Journal of Sustainable Development of Transport and Logistics, 2(2), 35–46.
   Google Scholar
• Mashhadi, M. M. R., Wulff, S. S., & Ksaibati, K. (2018). A comprehensive study of single and multiple truck crashes using violation and crash data. The Open Transportation Journal, 12(1), 43–56.
   Google Scholar
• Moomen, M., Rezapour, M., & Ksaibati, K. (2019). An investigation of influential factors of downgrade truck crashes: A logistic regression approach. Journal of Traffic and Transportation Engineering (English Edition), 6(2), 185–195.
   Google Scholar
• Najm, W. G., & Wiacek, C. J. (2000). Estimation of crash injury severity reduction for intelligent vehicle safety systems. SAE transactions, 1859–1865
   Google Scholar
• Nazif-Munoz, J. I., & Nikolic, N. (2018). The effectiveness of child restraint and seat belt legislation in reducing child injuries: The case of Serbia. Traffic Injury Prevention, 19(sup1), S7–S14. https://doi.org/10.1080/15389588.2017.1387254
   PubMed Web of Science ®Google Scholar
• O'donnell, C. J., & Connor, D. H. (1996). Predicting the severity of motor vehicle accident injuries using models of ordered multiple choice. Accident Analysis & Prevention, 28(6), 739–753.
   PubMed Web of Science ®Google Scholar
• Plummer, M. (2012). JAGS version 3.3. 0 user manual. International Agency for Research on Cancer., France.
   Google Scholar
• Rezapour, M., & Ksaibati, K. (2020). Application of Bayesian hierarchical finite mixture model to account for severe heterogeneous crash data. Signals, in press.
   Google Scholar
• Rezapour, M., Wulff, S. S., & Ksaibati, K. (2018). Predicting truck at-fault crashes using crash and traffic offence data. The Open Transportation Journal, 12(1), 128–138.
   Google Scholar
• Rezapour, W. K. (2018). Predicting truck at-fault crashes using crash and traffic offence data.
   Google Scholar
• Russo, B. J., & Savolainen, P. T. (2018). A comparison of freeway median crash frequency, severity, and barrier strike outcomes by median barrier type. Accident; Analysis and Prevention, 117, 216–224. https://doi.org/10.1016/j.aap.2018.04.023
   PubMed Web of Science ®Google Scholar
• Savolainen, P. T., Mannering, F. L., Lord, D., & Quddus, M. A. (2011). The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accident; Analysis and Prevention, 43(5), 1666–1676. https://doi.org/10.1016/j.aap.2011.03.025
   PubMed Web of Science ®Google Scholar
• Savolainen, P., & Mannering, F. (2007). Probabilistic models of motorcyclists' injury severities in single- and multi-vehicle crashes . Accident; Analysis and Prevention, 39(5), 955–963. https://doi.org/10.1016/j.aap.2006.12.016
   PubMed Web of Science ®Google Scholar
• Sen, B., Smith, J. D., & Najm, W. G. (2003). Analysis of lane change crashes (No. HS-809 571). National Highway Traffic Safety Administration.
   Google Scholar
• Shankar, V., & Mannering, F. (1996). An exploratory multinomial logit analysis of single-vehicle motorcycle accident severity. Journal of Safety Research, 27(3), 183–194. https://doi.org/10.1016/0022-4375(96)00010-2
   Web of Science ®Google Scholar
• Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583–639. https://doi.org/10.1111/1467-9868.00353
   Web of Science ®Google Scholar
• Stein, W. J., & Neuman, T. R. (2007). Mitigation strategies for design exceptions (No. FHWA-SA-07-011). Federal Highway Administration. Office of Safety.
   Google Scholar
• Tang, J., Liang, J., Han, C., Li, Z., & Huang, H. (2019). Crash injury severity analysis using a two-layer stacking framework. Accident; Analysis and Prevention, 122, 226–238. https://doi.org/10.1016/j.aap.2018.10.016
   PubMed Web of Science ®Google Scholar
• World Health Organization (2015). Global status report on road safety 2015. World Health Organization.
   Google Scholar
• Zhang, J., Lindsay, J., Clarke, K., Robbins, G., & Mao, Y. (2000). Factors affecting the severity of motor vehicle traffic crashes involving elderly drivers in Ontario. Accident; Analysis and Prevention, 32(1), 117–125. https://doi.org/10.1016/s0001-4575(99)00039-1
   PubMed Web of Science ®Google Scholar
• Zhu, X., & Srinivasan, S. (2011). A comprehensive analysis of factors influencing the injury severity of large-truck crashes. Accident; Analysis and Prevention, 43(1), 49–57. https://doi.org/10.1016/j.aap.2010.07.007
   PubMed Web of Science ®Google Scholar