Accident prediction models with random corridor parameters

Accident Analysis and Prevention

Volumn 41, Issue 5, 2009, Pages 1118-1123

  El Basyouny, Karim ^a   Sayed, Tarek ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Collision prediction models; Corridor variation; Full Bayes estimation; Markov Chain Monte Carlo; Random parameters

Indexed keywords

COLLISION PREDICTION MODELS; CORRIDOR VARIATION; FULL BAYES ESTIMATION; MARKOV CHAIN MONTE CARLO; RANDOM PARAMETERS;

MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS;

ACCIDENTS;

ANALYSIS OF VARIANCE; ARTICLE; BAYES THEOREM; CANADA; COMPARATIVE STUDY; HUMAN; POISSON DISTRIBUTION; PROBABILITY; RISK ASSESSMENT; STATISTICAL MODEL; STATISTICS; THEORETICAL MODEL; TRAFFIC ACCIDENT;

ACCIDENTS, TRAFFIC; ANALYSIS OF VARIANCE; BAYES THEOREM; BRITISH COLUMBIA; HUMANS; MARKOV CHAINS; MODELS, STATISTICAL; MODELS, THEORETICAL; POISSON DISTRIBUTION; RISK ASSESSMENT; STATISTICS AS TOPIC;

EID: 67949107931     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2009.06.025     Document Type: Article

References (48)

1. Aguero-Valverde J., and Jovanis P.P. Bayesian multivariate Poisson log-normal models for crash severity modeling and site ranking. Presented at the 88th Annual Meeting of the Transportation Research Board (2009)
2. Anastasopoulos P.Ch., and Mannering F. A note on modeling vehicle accident frequencies with random-parameters count models. Accident Analysis and Prevention 41 1 (2009) 153-159
3. Bedrick E.J., Christensen R., and Johnson W. A new perspective on priors for generalized linear models. Journal of the American Statistical Association 91 (1996) 1450-1460
4. Brooks S.P., and Gelman A. Alternative methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7 (1998) 434-455
5. Cheng W., and Washington S.P. Experimental evaluation of hotspot identification methods. Accident Analysis and Prevention 37 (2005) 870-881
6. El-Basyouny K., and Sayed T. Comparison of two negative binomial regression techniques in developing accident prediction models. Transportation Research Record 1950 (2006) 9-16
7. El-Basyouny, K., Sayed, T., 2009a. Urban arterial accident prediction models with spatial effects. Transportation Research Record, in press.
8. El-Basyouny K., and Sayed T. Collision prediction models using multivariate Poisson-lognormal regression. Accident Analysis and Prevention 41 4 (2009) 820-828
9. Gelman A., Meng X., and Stern H. Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica 6 (1996) 733-807
10. Hauer E. Observational Before-After Studies in Road Safety: Estimating the Effect of Highway and Traffic Engineering Measures on Road Safety (1997), Elsevier Science Ltd.
11. Heydecker B.G., and Wu J. Identification of sites for accident remedial work by Bayesian statistical methods: an example of uncertain inference. Advances in Engineering Software 32 (2001) 859-869
12. Hinde J., and Demetrio C.G.B. Over-dispersion: model and estimation. Computational Statistics and Data Analysis 27 2 (1998) 151-170
13. Kim H., Sun D., and Tsutakawa R.K. Lognormal vs. gamma: extra variations. Biometrical Journal 44 3 (2002) 305-323
14. Kumara S.S.P., and Chin H.C. Disaggregate models to examine signalized intersection crash frequencies. Presented at the 85th Annual Meeting of the Transportation Research Board. Washington, DC (2006)
15. Lee J., and Mannering F. Impact of roadside features on the frequency and severity of run-off-road accidents: an empirical analysis. Accident Analysis and Prevention 34 2 (2002) 349-361
16. Li W., Carriquiry A., Pawlovich M., and Welch T. The choice of statistical models in road safety countermeasures effectiveness studies in Iowa. Accident Analysis and Prevention 40 4 (2008) 1531-1542
17. Lord D., and Miranda-Moreno L.F. 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. Presented at 86th Annual Meeting of the Transportation Research Board. Washington, DC (2007)
18. Lord D., and Persaud B. Accident prediction models with and without trend: application of the generalized estimating equation. Transportation Research Record 1717 (2000) 102-108
19. Lord, D., 2000. The prediction of accidents on digital networks: characteristics and issues related to the application of accident prediction models. Ph.D. Dissertation. Department of Civil Engineering, University of Toronto, Toronto, Ontario.
20. Lord D., Guikema S.D., and Geedipally S.R. Application of the Conway-Maxwell-Poisson generalized linear model for analyzing motor vehicle crashes. Accident Analysis and Prevention 40 (2008) 1123-1134
21. Lord D., and Park Y.-J. Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates. Accident Analysis and Prevention 40 4 (2008) 1441-1457
22. Lord D., Washington S.P., and Ivan J.N. 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
23. Lord D., Washington S.P., and Ivan J.N. Further notes on the application of zero inflated models in highway safety. Accident Analysis and Prevention 39 1 (2007) 53-57
24. Lunn D.J., Thomas A., Best N., and Spiegelhalter D. WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 10 (2000) 325-337
25. Ma J., Kockelman K.M., and Damien P. A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods. Accident Analysis and Prevention 40 (2008) 964-975
26. Malyshkina N.V., Mannering F.L., and Tarko A.P. Markov switching negative binomial models: an application to vehicle accident frequencies. Accident Analysis and Prevention 41 2 (2009) 217-226
27. Miaou S., and Song J. Bayesian ranking of sites for engineering safety improvements: decision parameter, treatability concept, statistical criterion, and spatial dependence. Accident Analysis and Prevention 37 4 (2005) 699-720
28. Miaou S.P., and Lord D. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes. Transportation Research Record 1840 (2003) 31-40
29. Milton J., Shankar V., and Mannering F. Highway accident severities and the mixed logit model: an exploratory empirical analysis. Accident Analysis and Prevention 40 1 (2008) 260-266
30. Miranda-Moreno L.F., and Lord D. Evaluation of alternative hyper-priors for Bayesian road safety analysis. Presented at the 87th Annual Meeting of the Transportation Research Board. Washington, DC (2007)
31. Miranda-Moreno L.F., Fu L., Saccomanno F.F., and Labbe A. Alternative risk models for ranking locations for safety improvement. Transportation Research Record 1908 (2005) 1-8
32. Mitra S., and Washington S. On the nature of over-dispersion in motor vehicle crash prediction models. Accident Analysis and Prevention 39 (2007) 459-468
33. Park E.S., and Lord D. Multivariate Poisson-lognormal models for jointly modeling crash frequency by severity. Transportation Research Record 2019 (2007) 1-6
34. Poch M., and Mannering F.L. Negative binomial analysis of intersection-accident frequency. Journal of Transportation Engineering 122 2 (1996) 105-113
35. Qin X., Ivan J.N., and Ravishankar N. Selecting exposure measures in crash rate prediction for two-lane highway segments. Accident Analysis and Prevention 36 2 (2004) 183-191
36. Qin X., Ivan J.N., Ravishanker N., and Liu J. Hierarchical Bayesian estimation of safety performance functions for two-lane highways using Markov Chain Monte Carlo modeling. Journal of Transportation Engineering 131 5 (2005) 345-351
37. Sawalha Z., and Sayed T. Traffic accidents modeling: some statistical issues. Canadian Journal of Civil Engineering 33 9 (2006) 1115-1124
38. Sawalha Z., and Sayed T. Evaluating safety of urban arterial roadways. Journal of Transportation Engineering 127 2 (2001) 151-158
39. Schluter P.J., Deely J.J., and Nicholson A.J. Ranking and selecting motor vehicle accident sites by using a hierarchical Bayesian model. The Statistician 46 3 (1997) 293-316
40. Shankar V.N., Albin R.B., Milton J.C., and Mannering F.L. Evaluating median cross-over likelihoods with clustered accident counts: an empirical inquiry using the random effects negative binomial model. Transportation Research Record 1635 (1998) 44-48
41. Shankar V.N., Chayanan S., Sittikariya, Shyu M.-B., Juvva N.K., and Milton J.C. Marginal impacts of design, traffic, weather, and related interactions on roadside crashes. Transportation Research Record 1897 (2004) 156-163
42. Spiegelhalter, D., Thomas, A., Best, N., Lunn, D., 2005. WinBUGS User Manual. MRC Biostatistics Unit, Cambridge. Available from http://www.mrc-cam.ac.uk/bugs.
43. Spiegelhalter D.J., Best N.G., Carlin B.P., and Van der Linde A. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society B 64 (2002) 1-34
44. Stern H.S., and Cressie N. Posterior predictive model checks for disease mapping models. Statistics in Medicine 19 (2000) 2377-2397
45. Tunaru R. Hierarchical Bayesian models for multiple count data. Austrian Journal of Statistics 31 3 (2002) 221-229
46. Warton D.I. Many zeros does not mean zero inflation: comparing the goodness-of-fit of parametric models to multivariate abundance data. Environmetrics 16 2 (2005) 275-289
47. Winkelmann R. Econometric Analysis of Count Data (2003), Springer, Germany
48. Ye X., Pendyala R.M., Washington S.P., Konduri K., and Oh J. A simultaneous equations model of crash frequency by collision type for rural intersections. Safety Science 47 3 (2009) 443-452