Safety evaluation for regulation changes on commercial vehicle operation using multilevel Bayesian methods

References

• Adams-Guppy, J., & Guppy, A. (2003). Truck driver fatigue risk assessment and management: A multinational survey. Ergonomics, 46(8), 763–779. https://doi.org/10.1080/0014013021000056980
   PubMed Web of Science ®Google Scholar
• Akbarinia, F., & Behnood, H. R. (2023). A bootstrap-based Markov chain Monte Carlo reliability assessment of crash modification factors calculated by full Bayesian method. Innovative Infrastructure Solutions, 8(1), 53. https://doi.org/10.1007/s41062-022-01018-0
   Web of Science ®Google Scholar
• Besag, J., Green, P., Higdon, D., & Mengersen, K. (1995). Bayesian computation and stochastic systems. Statistical Science, 10(1), 3–41. https://www.jstor.org/stable/2246224 https://doi.org/10.1214/ss/1177010123
   Web of Science ®Google Scholar
• Bonneson, J. A. (2010). Highway safety manual. Washington, DC, USA: American Association of State Highway and Transportation Officials (AASHTO).
   Google Scholar
• Bunn, T. L., Slavova, S., & Rock, P. J. (2019). Association between commercial vehicle driver at-fault crashes involving sleepiness/fatigue and proximity to rest areas and truck stops. Accident; Analysis and Prevention, 126, 3–9. https://doi.org/10.1016/j.aap.2017.11.022
   PubMed Web of Science ®Google Scholar
• Chen, C., & Xie, Y. (2014). The impacts of multiple rest-break periods on commercial truck driver’s crash risk. Journal of Safety Research, 48, 87–93. https://doi.org/10.1016/j.jsr.2013.12.003
   PubMed Web of Science ®Google Scholar
• Crum, M. R., Morrow, P. C., Olsgard, P., & Roke, P. J. (2001). Truck driving environments and their influence on driver fatigue and crash rates. Transportation Research Record, 1779(1), 125–133. https://doi.org/10.3141/1779-17
   Google Scholar
• Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995). Bayesian data analysis. Chapman and Hall/CRC.
   Google Scholar
• Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing, 24(6), 997–1016. https://doi.org/10.1007/s11222-013-9416-2
   Web of Science ®Google Scholar
• Hauer, E. (1997). Observational before/after studies in road safety: Estimating the effect of highway and traffic engineering measures on road safety. Pergamon.
   Google Scholar
• Hauer, E., Harwood, D. W., Council, F. M., & Griffith, M. S. (2002). Estimating safety by the empirical Bayes method: A tutorial. Transportation Research Record, 1784(1), 126–131. https://doi.org/10.3141/1784-16
   Google Scholar
• Haworth, N. L., Heffernan, C. J., & Horne, E. J. (1989). Fatigue in truck accidents (No. Report 3). Victorian Road Freight Industry Council.
   Google Scholar
• Howlader, M. M., Yasmin, S., Bhaskar, A., & Haque, M. M. (2023). A before-after evaluation of protected right-turn signal phasings by applying Empirical Bayes and Full Bayes approaches with heterogenous count data models. Accident; Analysis and Prevention, 179, 106882. https://doi.org/10.1016/j.aap.2022.106882
   PubMed Web of Science ®Google Scholar
• Islam, M. T., & El-Basyouny, K. (2015). Full Bayesian evaluation of the safety effects of reducing the posted speed limit in urban residential area. Accident; Analysis and Prevention, 80, 18–25. https://doi.org/10.1016/j.aap.2015.02.026
   PubMed Web of Science ®Google Scholar
• Islam, M. T., El-Basyouny, K., Ibrahim, S. E., & Sayed, T. (2016). Before–after safety evaluation using full Bayesian macroscopic multivariate and spatial models. Transportation Research Record, 2601(1), 128–137. https://doi.org/10.3141/2601-15
   Google Scholar
• Kim, D., Kim, S., Choi, J., & Cho, J. (2017). The statistical correlation between continuous driving time and drowsy accidents. Journal of Korean Society of Transportation, 35(5), 423–433. https://doi.org/10.7470/jkst.2017.35.5.423
   Google Scholar
• Kodi, J. H., Kitali, A. E., Ali, M. S., Alluri, P., & Sando, T. (2021). Estimating safety impacts of adaptive signal control technology using a full Bayesian approach. Transportation Research Record, 2675(11), 1168–1179. https://doi.org/10.1177/0361198121102528
   Web of Science ®Google Scholar
• Korea Expressway Corporation Research Institute. (2018). Analysis of continuous driving time based on T map data Korea.
   Google Scholar
• Kwon, S., Kim, H., Kim, G. S., & Cho, E. (2019). Fatigue and poor sleep are associated with driving risk among Korean occupational drivers. Journal of Transport & Health, 14, 100572. https://doi.org/10.1016/j.jth.2019.100572
   Web of Science ®Google Scholar
• Lan, B., Persaud, B., Lyon, C., & Bhim, R. (2009). Validation of a full Bayes methodology for observational before–after road safety studies and application to evaluation of rural signal conversions. Accident; Analysis and Prevention, 41(3), 574–580. https://doi.org/10.1016/j.aap.2009.02.010
   PubMed Web of Science ®Google Scholar
• Lee, C., Abdel-Aty, M., Park, J., & Wang, J. H. (2015). Development of crash modification factors for changing lane width on roadway segments using generalized nonlinear models. Accident; Analysis and Prevention, 76, 83–91. https://doi.org/10.1016/j.aap.2015.01.007
   PubMed Web of Science ®Google Scholar
• Lu, J., Gan, A., Haleem, K., & Wu, W. (2013). Clustering-based roadway segment division for the identification of high-crash locations. Journal of Transportation Safety & Security, 5(3), 224–239. https://doi.org/10.1080/19439962.2012.730118
   Google Scholar
• Oron-Gilad, T., & Shinar, D. (2000). Driver fatigue among military truck drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 3(4), 195–209. https://doi.org/10.1016/S1369-8478(01)00004-3
   Google Scholar
• Park, B. J., Lord, D., & Wu, L. (2016). Finite mixture modeling approach for developing crash modification factors in highway safety analysis. Accident; Analysis and Prevention, 97, 274–287. https://doi.org/10.1016/j.aap.2016.10.023
   PubMed Web of Science ®Google Scholar
• Park, J., & Abdel-Aty, M. (2015). Development of adjustment functions to assess combined safety effects of multiple treatments on rural two-lane roadways. Accident; Analysis and Prevention, 75, 310–319. https://doi.org/10.1016/j.aap.2014.12.012
   PubMed Web of Science ®Google Scholar
• Park, J., Abdel-Aty, M., & Lee, C. (2014). Exploration and comparison of crash modification factors for multiple treatments on rural multilane roadways. Accident; Analysis and Prevention, 70, 167–177. https://doi.org/10.1016/j.aap.2014.03.016
   PubMed Web of Science ®Google Scholar
• Park, J., Abdel-Aty, M., & Lee, J. (2016). Use of empirical and full Bayes before–after approaches to estimate the safety effects of roadside barriers with different crash conditions. Journal of Safety Research, 58, 31–40. https://doi.org/10.1016/j.jsr.2016.06.002
   PubMed Web of Science ®Google Scholar
• Park, J., Abdel-Aty, M., & Wang, J. H. (2017). Time series trends of the safety effects of pavement resurfacing. Accident; Analysis and Prevention, 101, 78–86. https://doi.org/10.1016/j.aap.2017.02.006
   PubMed Web of Science ®Google Scholar
• Park, J., Abdel-Aty, M., Wang, J. H., & Lee, C. (2015). Assessment of safety effects for widening urban roadways in developing crash modification functions using nonlinearizing link functions. Accident; Analysis and Prevention, 79, 80–87. https://doi.org/10.1016/j.aap.2015.03.025
   PubMed Web of Science ®Google Scholar
• Persaud, B., & Lyon, C. (2007). Empirical Bayes before–after safety studies: Lessons learned from two decades of experience and future directions. Accident Analysis & Prevention, 39(3), 546–555. https://doi.org/10.1016/j.aap.2006.09.009
   PubMed Web of Science ®Google Scholar
• Persaud, B., Lan, B., Lyon, C., & Bhim, R. (2010). Comparison of empirical Bayes and full Bayes approaches for before–after road safety evaluations. Accident; Analysis and Prevention, 42(1), 38–43. https://doi.org/10.1016/j.aap.2009.06.028
   PubMed Web of Science ®Google Scholar
• Pu, Z., Li, Z., Jiang, Y., & Wang, Y. (2021). Full Bayesian before-after analysis of safety effects of variable speed limit system. IEEE Transactions on Intelligent Transportation Systems, 22(2), 964–976. https://doi.org/10.1109/TITS.2019.2961699
   Web of Science ®Google Scholar
• Sacchi, E., & Sayed, T. (2014). Accounting for heterogeneity among treatment sites and time trends in developing crash modification functions. Accident; Analysis and Prevention, 72, 116–126. https://doi.org/10.1016/j.aap.2014.06.016
   PubMed Web of Science ®Google Scholar
• Sacchi, E., & Sayed, T. (2016). Bayesian estimation of conflict-based safety performance functions. Journal of Transportation Safety & Security, 8(3), 266–279. https://doi.org/10.1080/19439962.2015.1030807
   Web of Science ®Google Scholar
• Sacchi, E., Sayed, T., & El-Basyouny, K. (2014). Collision modification functions: Incorporating changes over time. Accident; Analysis and Prevention, 70, 46–54. https://doi.org/10.1016/j.aap.2014.03.003
   PubMed Web of Science ®Google Scholar
• Sagberg, F., Jackson, P., Krüger, H. P., Muzet, A., & Williams, A. J. (2004). Fatigue, sleepiness and reduced alertness as risk factors in driving. Institute of Transport Economics.
   Google Scholar
• Watanabe, S., & Opper, M. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11(12), 3571–3594.
   Google Scholar