Application of Bayesian informative priors to enhance the transferability of safety performance functions

Journal of Safety Research

Volumn 62, Issue , 2017, Pages 155-161

  Farid, Ahmed ^a   Abdel Aty, Mohamed ^a   Lee, Jaeyoung ^a   Eluru, Naveen ^a  

^a University of Central Florida   (United States)

Author keywords

Bayesian informative priors; Highway safety Manual; Markov chain Monte Carlo simulations; Negative binomial models; Transferability

Indexed keywords

BAYESIAN NETWORKS; HIGHWAY ENGINEERING; INFERENCE ENGINES; MARKOV PROCESSES; MONTE CARLO METHODS; SAFETY ENGINEERING; TRANSPORTATION;

HIGHWAY SAFETY; INFORMATIVE PRIORS; MARKOV CHAIN MONTE CARLO SIMULATION; NEGATIVE BINOMIAL MODELS; TRANSFERABILITY;

HIGHWAY ACCIDENTS;

BAYES THEOREM; CALIFORNIA; ENVIRONMENTAL PLANNING; FLORIDA; PREVENTION AND CONTROL; SAFETY; TRAFFIC ACCIDENT;

ACCIDENTS, TRAFFIC; BAYES THEOREM; CALIFORNIA; ENVIRONMENT DESIGN; FLORIDA; SAFETY;

EID: 85022100358     PISSN: 00224375     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jsr.2017.06.005     Document Type: Article

References (35)

1. AASHTO, Highway safety manual. 2010, American Association of State Highway and Transportion Officials, Washington D.C.
2. Abdel-Aty, M., Lee, J., Siddiqui, C., Choi, K., Geographical unit based analysis in the context of transportation safety planning. Transportation Research Part A: Policy and Practice 49 (2013), 62–75.
3. Cowles, M., Applied Bayesian statistics with R and OpenBUGS examples. 2013, Springer, New York.
4. Donnell, E., Mason, J., Predicting the frequency of median barrier crashes on Pennsylvania interstate highways. Accident Analysis and Prevention 38:3 (2006), 590–599.
5. Farid, A., Abdel-Aty, M., Lee, J., Eluru, N., Wang, J.-H., Exploring the transferability of safety performance functions. Accident Analysis and Prevention 94 (2016), 143–152.
6. Florida Statutes, Title XXIII – Motor vehicle. 2016, The Florida legislature, Tallahassee, Florida http://www.leg.state.fl.us/Statutes/index.cfm?App_mode=Display_Statute&Search_String=&URL=0300-0399/0316/Sections/0316.065.html Accessed Oct. 28, 2015.
7. Garber, N., Wu, L., Stochastic models relating crash probabilities with geometric and corresponding traffic characteristics data. Presented to National Intelligent Transportation Systems Implementation Research Center of the Department of Transportation, Charlottesville, Virginia, 2001.
8. Haleem, K., Abdel-Aty, M., Mackie, K., Using a reliability process to reduce uncertainty in predicting crashes at unsignalized intersections. Accident Analysis and Prevention 42:2 (2010), 654–666.
9. Harnen, S., Umar, R., Wong, S., Hashim, W., Development of prediction models for motorcycle crashes at signalized intersections on urban roads in Malaysia. Journal of Transportation and Statistics 7:2 (2004), 27–39.
10. Heydari, S., Miranda-Moreno, L., Lord, D., Fu, L., Bayesian methodology to estimate and update safety performance functions under limited data conditions: A sensitivity analysis. Accident Analysis and Prevention 64 (2014), 41–51.
11. Highway Safety Information System. State data, Highway Safety Research Center. 2015, University of North Carolina, Chapel Hill, North Carolina (Accessed Sept. 28, 2015) http://www.hsisinfo.org/.
12. Jang, H., Lee, S., Kim, S., Bayesian analysis for zero-inflated regression models with the power prior: Applications to road safety countermeasures. Accident Analysis and Prevention 42 (2010), 540–547.
13. Kim, S., Chung, S., Song, K., Chon, K., Development of an accident prediction model using GLIM (generalized log-linear model) and EB method: A case of Seoul. Journal of the Eastern Asia Society for Transportation Studies 6 (2005), 3669–3682.
14. Lee, J., Abdel-Aty, M., Choi, K., Analysis of residence characteristics of at-fault drivers in traffic crashes. Safety Science 68 (2014), 6–13.
15. Lee, J., Abdel-Aty, M., Choi, K., Huang, H., Multi-level hot zone identification for pedestrian safety. Accident Analysis and Prevention 76 (2015), 64–73.
16. Lee, J., Abdel-Aty, M., Jiang, X., Development of zone system for macro-level traffic safety analysis. Journal of Transport Geography 38 (2014), 13–21.
17. Lee, J., Abdel-Aty, M., Jiang, X., Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level. Accident Analysis and Prevention 78 (2015), 146–154.
18. Lord, D., Miranda-Moreno, L., Effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter of Poisson-gamma models for modeling motor vehicle crashes: A Bayesian perspective. Safety Science 46:5 (2008), 751–770.
19. Lord, D., Washington, S., Ivan, J., Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: Balancing statistical fit and theory. Accident Analysis and Prevention 37:1 (2005), 35–46.
20. Lunn, D., Thomas, A., Best, N., Spiegelhalter, D., WinBUGS—A Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing 10:4 (2000), 325–337.
21. Ma, J., Kockelman, K., Damien, P., A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods. Accident Analysis and Prevention 40:3 (2008), 964–975.
22. Miaou, S.-P., The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accident Analysis and Prevention 26:4 (1994), 471–482.
23. Miaou, S.-P., Lum, H., Modeling vehicle accidents and highway geometric design relationships. Accident Analysis and Prevention 25:6 (1993), 689–709.
24. Miranda-Moreno, L., Heydari, S., Lord, D., Fu, L., Bayesian road safety analysis: Incorporation of past evidence and effect of hyper-prior choice. Journal of Safety Research 46 (2013), 31–40.
25. Mitra, S., Washington, S., On the nature of over-dispersion in motor vehicle crash prediction models. Accident Analysis and Prevention 39:3 (2007), 459–468.
26. Natarajan, R., McCulloch, C., Gibbs sampling with diffuse proper priors: A valid approach to data-driven inference?. Journal of Computational and Graphical Statistics 7:3 (1998), 267–277.
27. Park, B.-J., Lord, D., Hart, J., Bias properties of Bayesian statistics in finite mixture of negative binomial regression models in crash data analysis. Accident Analysis and Prevention 42:2 (2010), 741–749.
28. R Core Team, R: A language and environment for statistical computing. 2011, R Foundation for Statistical Computing, Vienna, Austria (ISBN 3-900051-07-0, 2013. http://www.r-project.org/. Accessed Jan. 20, 2016).
29. Sawalha, Z., Sayed, T., Traffic accident modeling: Some statistical issues. Canadian Journal of Civil Engineering 33:9 (2006), 1115–1124.
30. Spiegelhalter, D., Best, N., Carlin, B., van der Linde, A., Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B 64:4 (2002), 583–639.
31. Sturtz, S., Ligges, U., Gelman, A., R2WinBUGS: A package for running WinBUGS from R. Journal of Statistical Software 12:3 (2005), 1–16.
32. Taylor, M., Baruya, A., Kennedy, J., The relationship between speed and accidents on rural single-carriageway roads. Publication TRL511, 2002, United Kingdom, Transport Research Laboratory.
33. Washington, S., Oh, J., Bayesian methodology incorporating expert judgment for ranking countermeasure effectiveness under uncertainty: Example applied to at grade railroad crossings in Korea. Accident Analysis and Prevention 38 (2006), 234–247.
34. Xie, F., Gladhill, K., Dixon, K., Monsere, C., Calibration of highway safety manual predictive models for Oregon state highways. Transportation Research Record: Journal of the Transportation Research Board 2241 (2011), 19–28.
35. Yu, R., Abdel-Aty, M., Investigating different approaches to develop informative priors in hierarchical Bayesian safety performance functions. Accident Analysis and Prevention 56 (2013), 51–58.