Modeling the equivalent property damage only crash rate for road segments using the hurdle regression framework

Analytic Methods in Accident Research

Volumn 11, Issue , 2016, Pages 48-61

  Ma, Lu ^a   Yan, Xuedong ^a   Wei, Chong ^a   Wang, Jiangfeng ^a  

^a BEIJING JIAOTONG UNIVERSITY   (China)

Author keywords

Accident loss; Equivalent property damage only rate; Hurdle model; Mixed distribution; Tobit model

Indexed keywords

CODES (SYMBOLS); ROADS AND STREETS;

ACCIDENT LOSS; EXPLANATORY VARIABLES; MIXED DISTRIBUTION; MODEL PERFORMANCE; PREDICTION ACCURACY; PROPERTY DAMAGE ONLY; RANDOM PARAMETERS; SAFETY MANAGEMENT;

HIGHWAY PLANNING;

EID: 84981343566     PISSN: 22136657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amar.2016.07.001     Document Type: Article

References (48)

1. Abdel-Aty, M., Radwan, E., Modeling traffic accident occurrence and involvement. Accident Analysis and Prevention 32:5 (2000), 633–642.
2. Anastasopoulos, P., Random parameters multivariate tobit and zero-inflated count data models: addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis. Analytic Methods in Accident Research 11 (2016), 17–32.
3. Anastasopoulos, P., Tarko, A., Mannering, F., Tobit analysis of vehicle accident rates on interstate highways. Accident Analysis and Prevention 40:2 (2008), 768–775.
4. Anastasopoulos, P., Mannering, F., Shankar, V., Haddock, J., A study of factors affecting highway accident rates using the random-parameters tobit model. Accident Analysis and Prevention 45 (2012), 628–633.
5. Anastasopoulos, P., Shankar, V., Haddock, J., Mannering, F., A multivariate tobit analysis of highway accident-injury-severity rates. Accident Analysis and Prevention 45 (2012), 110–119.
6. Barua, S., El-Basyouny, K., Islam, M., Multivariate random parameters collision count data models with spatial heterogeneity. Analytic Methods in Accident Research 9 (2016), 1–15.
7. Bhat, C., Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model. Transportation Research Part B 35:7 (2001), 677–693.
8. Bolancé, C., Guillen, M., Nielsen, J., Kernel density estimation of actuarial loss functions. Insurance: Mathematics and Economics 32:1 (2003), 19–36.
9. Bouchera, J., Santolino, M., Discrete distributions when modeling the disability severity score of motor victims. Accident Analysis and Prevention 42:6 (2010), 2041–2049.
10. Boudreau, N., 2014. Traffic and Safety Engineering Project Selection Process. Available at 〈https://www.massdot.state.ma.us/Portals/17/docs/PSA/Traffic_Safety3–13-14_Final.pdf〉 (accessed 03.24.16).
11. Caird, J., Chisholm, S., Lockhart, J., Do in-vehicle advanced signs enhance older and younger drivers’ intersection performance? Driving simulation and eye movement results. International Journal of Human-Computer Studies 66:3 (2008), 132–144.
12. Campbell, J., Knapp, K., 2005. Alternative crash severity ranking measures and the implications on crash severity ranking procedures. In: Proceedings of the 2005 Mid-Continent Transportation Research Symposium, Ames, Iowa, August 2005.
13. Chiou, Y., Fu, C., Modeling crash frequency and severity using multinomial-generalized Poisson model with error components. Accident Analysis and Prevention 50 (2013), 73–82.
14. Cragg, J., Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica 39:5 (1971), 829–844.
15. Eling, M., Fitting insurance claims to skewed distributions: are the skew-normal and skew-student good models?. Insurance: Mathematics and Economics 51:2 (2012), 239–248.
16. Felsburg Holt, Ullevig, 2035 Regional Transportation Plan Update, Safety Analysis-procedural Overview. 2008, Prepared for Denver Regional Council of Governments, Centennial, Colorado.
17. Gregoriades, A., Mouskos, K., Black spots identification through a Bayesian Networks quantification of accident risk index. Transportation Research Part C 28 (2013), 28–43.
18. Harkey, D., Evaluation of truck crashes using a GIS-based crash referencing and analysis system. Transportation Research Record 1686 (1999), 13–21.
19. HRPDC, 2006. Regional Crash Database Supports Safety Planning and Drives State Crash Data Improvement. Available at 〈http://tsp.trb.org/assets/BP04_Hampton_Final.pdf〉 (accessed 03.24.16).
20. Hunter, W., Stewart, J., Eccles, K., Huang, H., Council, F., Harkey, D., Three-strand cable median barrier in North Carolina: in-service evaluation. Transportation Research Record 1743 (2001), 97–103.
21. Jones, A., A double-hurdle model of cigarette consumption. Journal of Applied Econometrics 4:1 (1989), 23–39.
22. Kononov, J., Bailey, B., Allery, B., 2008. Exploratory analysis of relationship between the number of lanes and safety on urban freeways. In: Proceedings of the 87th Annual Meeting of the Transportation Research Board. Washington, D.C.
23. Lee, C., Abdel-Aty, M., Park, J., Wang, J., Development of crash modification factors for changing lane width on roadway segments using generalized nonlinear models. Accident Analysis and Prevention 76 (2015), 83–91.
24. Lehmann, E., Casella, G., Theory of Point Estimation. 1998, Springer, New York.
25. Lord, D., Mannering, F., The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives. Transportation Research Part A 44:5 (2010), 291–305.
26. Ma, J., Kockelman, K., Bayesian multivariate Poisson regression for models of injury count by severity. Transportation Research Record 1950 (2006), 24–34.
27. Ma, L., Yan, X., Weng, J., Modeling traffic crash rates of road segments through a lognormal hurdle framework with flexible scale parameter. Journal of Advanced Transportation 49:8 (2015), 928–940.
28. Ma, L., Wang, G., Yan, X., Weng, J., A hybrid finite mixture model for exploring heterogeneous ordering patterns of driver injury severity. Accident Analysis and Prevention 89 (2016), 62–73.
29. Ma, L., Yan, X., Zhang, C., Weng, J., 2014. Traffic accident risk estimation based on the lognormal hurdle model with a flexible scale parameter. In: Proceedings of the 93rd Annual Meeting of the Transportation Research Board, Washington, D.C.
30. Maddala, G., Limited-dependent and Qualitative Variables in Econometrics. 1983, Cambridge University Press, Cambridge, United Kingdom.
31. Mannering, F., An empirical analysis of driver perceptions of the relationship between speed limits and safety. Transportation Research Part F 12:2 (2009), 99–106.
32. Mannering, F., Bhat, C., Analytic methods in accident research: Methodological frontier and future directions. Analytic Methods in Accident Research 1 (2014), 1–22.
33. Mannering, F., Shankar, V., Bhat, C., Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic Methods in Accident Research 11 (2016), 1–16.
34. Martínez-Espiñeira, R., A box-cox double-hurdle model of wildlife valuation: the citizen's perspective. Ecological Economics 58:1 (2006), 192–208.
35. Miller, P., Moffet, J., The Price of Mobility: Uncovering the Hidden Costs of Transportation. 1993, Natural Resources Defense Council, New York.
36. Montella, A., A comparative analysis of black spot identification methods. Accident Analysis and Prevention 42:2 (2010), 571–581.
37. Oh, J., Washington, S., Lee, D., Property damage crash equivalency factors to solve crash frequency-severity dilemma: case study on South Korean rural roads. Transportation Research Record 2148 (2010), 83–92.
38. Ozbay, K., Bartin, B., Berechman, J., Estimation and evaluation of full marginal costs of highway transportation in New Jersey. Journal of Transportation and Statistics 4:1 (2001), 81–103.
39. PIARC, World Road Association, Technical Committee on Road Safety C13, 2004. Road Safety Manual. Switzerland.
40. Rifaat, S., Tay, R., De Barros, A., Effect of street pattern on road safety: are policy recommendations sensitive to aggregations of crashes by severity?. Transportation Research Record 2147 (2010), 58–65.
41. Shankar, V., Milton, J., Mannering, F., Modeling accident frequencies as zero-altered probability processes: an empirical inquiry. Accident Analysis and Prevention 29:6 (1997), 829–837.
42. Suhaila, J., Ching-Yee, K., Fadhilah, Y., Hui-Mean, F., Introducing the mixed distribution in fitting rainfall data. Opening Journal of Modern Hydrology 1:2 (2011), 11–22.
43. Tobin, J., Estimation of relationships for limited dependent variables. Econometrica 26:1 (1958), 24–36.
44. UMassSafe, 2011. Equivalent Property Damage Only Ranking of Commercial Vehicle Crashes. Available at 〈http://cmvtoolkit.ecs.umass.edu/EPDO_2011_Report.pdf〉 (accessed 03.24.16).
45. Vilar, J., Cao, R., Ausín, M., González-Fragueiro, C., Nonparametric analysis of aggregate loss models. Journal of Applied Statistics 36:2 (2009), 149–166.
46. Washington, S., Haque, M., Oh, J., Lee, D., Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots. Accident Analysis and Prevention 66 (2014), 136–146.
47. Xiong, Y., Mannering, F., The heterogeneous effects of guardian supervision on adolescent driver-injury severities: a finite-mixture random-parameters approach. Transportation Research Part B 49 (2013), 39–54.
48. Zhu, X., Srinivasan, S., A comprehensive analysis of factors influencing the injury severity of large-truck crashes. Accident Analysis and Prevention 43:1 (2011), 49–57.