Urban Arterial Accident Prediction Models with Spatial Effects
Abstract
This paper investigates the inclusion of spatial effects in accident prediction models. Two types of spatial modeling techniques–-the Gaussian conditional autoregressive (CAR) and the multiple membership (MM) models–-were compared with the traditional Poisson–lognormal model. A variation of the MM model (extended MM or EMM) was also investigated to study the effect of clustering segments within the same corridor on spatial correlation. Full Bayes estimation was used by means of the Markov chain Monte Carlo methodology to estimate the parameters. The study made use of 281 urban road segments in Vancouver, British Columbia, Canada. Various traffic and geometric variables were included in the accident prediction models. The models were compared in terms of their goodness of fit and inference. For the data set under consideration, the results showed that annual average daily traffic, business land use, the number of lanes between signals, and the density of unsignalized intersections have significant positive impact on the number of accidents. The fitted CAR and MM models had significant estimates for both heterogeneity and spatial correlation parameters. The best-fit model was EMM, followed by CAR. Furthermore, a significant portion of the total variability was explained by the spatial correlation. A significant correlation was also found between the heterogeneity and spatial effects. This may be because neighboring road segments typically have similar environmental and geographic characteristics and thereby form a cluster with similar accident occurrence. The results also showed that corridor variation was a major component of total variability and that the spatial effects have been considerably alleviated by clustering segments within the same corridor.
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References
1. Hauer E., Ng J. C. N., and Lovell J. Estimation of Safety at Signalized Intersections. In Transportation Research Record 1185, TRB, National Research Council, Washington, D.C., 1988, pp. 48–61.
2. Sawalha Z., and Sayed T. Traffic Accidents Modeling: Some Statistical Issues. Canadian Journal of Civil Engineering, Vol. 33, No. 9, 2006, pp. 1115–1124.
3. Hauer E. Observational Before–After Studies in Road Safety: Estimating the Effect of Highway and Traffic Engineering Measures on Road Safety. Elsevier Science, Ltd., 1997.
4. Hinde J., and Demetrio C. G. B. Over-Dispersion: Model and Estimation. Computational Statistics and Data Analysis, Vol. 27, No. 2, 1998, pp. 151–170.
5. Kim H., Sun D., and Tsutakawa R. K. Lognormal vs. Gamma: Extra Variations. Biometrical Journal, Vol. 44, No. 3, 2002, pp. 305–323.
6. Lord D., and Miranda-Moreno L. F. Effects of Low Sample Mean Values and Small Sample Size on Estimation of Fixed Dispersion Parameter of Poisson–Gamma Models for Motor Vehicle Crashes: Bayesian Perspective. Presented at 86th Annual Meeting of the Transportation Research Board, Washington, D.C., 2007.
7. Winkelmann R. Econometric Analysis of Count Data. Springer, Germany, 2003.
8. Maher M. J., and Summersgill I. A Comprehensive Methodology for the Fitting of Predictive Accident Models. Accident Analysis and Prevention, Vol. 28, No. 3, 1996, pp. 281–296.
9. Wood G. R. Generalized Linear Accident Models and Goodness of Fit Testing. Accident Analysis and Prevention, Vol. 34, No. 4, 2002, pp. 417–427.
10. Lord D. Modeling Motor Vehicle Crashes Using Poisson–Gamma Models: Examining the Effects of Low Sample Mean Values and Small Sample Size on the Estimation of the Fixed Dispersion Parameter. Accident Analysis and Prevention, Vol. 38, No. 4, 2006, pp. 751–766.
11. Aguero-Valverde J., and Jovanis P. P. Analysis of Road Crash Frequency with Spatial Models. In Transportation Research Record: Journal of the Transportation Research Board, No. 2061, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 55–63.
12. Ghosh M., Natarajan K., Waller L. A., and Kim D. Hierarchical Bayes for the Analysis of Spatial Data: An Application to Disease Mapping. Journal of Statistical Planning and Inference, Vol. 75, 1999, pp. 305–318.
13. Sun D., Tsutakawa R. K., Kim H., and He Z. Spatio-Temporal Interaction with Disease Mapping. Statistics in Medicine, Vol. 19, 2000, pp. 2015–2035.
14. Miaou S., Song J. J., and Mallick B. K. Roadway Traffic Crash Mapping: A Space–Time Modeling Approach. Journal of Transportation and Statistics, Vol. 6, No. 1, 2003, pp. 33–57.
15. Congdon P. Bayesian Statistical Modeling, 2nd ed. Wiley, New York, 2006.
16. Besag J., York J., and Mollié A. Bayesian Image Restoration with Two Applications in Spatial Statistics. Annals of the Institute of Statistical Mathematics, Vol. 43, 1991, pp. 1–75.
17. Goldstein H. Multilevel Statistical Models. Arnold, London, 1995.
18. Goldstein H., Rasbash J., Plewis I., Draper D., Browne W., Yang M., Woodhouse G., and Healy H. A User's Guide to MLwiN. Institute of Education, London, 1998.
19. Langford I. H., Leyland A. H., Rasbash J., and Goldstein H. Multilevel Modeling of the Geographical Distributions of Diseases. Applied Statistics, Vol. 48, No. 2, 1999, pp. 253–268.
20. Lord D., and Persaud B. N. Accident Prediction Models With and Without Trend: Application of the Generalized Estimating Equations Procedure. In Transportation Research Record: Journal of the Transportation Research Board, No. 1717, TRB, National Research Council, Washington, D.C., 2000, pp. 102–108.
21. Levine N., Kim K. E., and Nitz L. H. Spatial Analysis of Honolulu Motor Vehicle Crashes (I): Spatial Patterns. Accident Analysis and Prevention, Vol. 27, No. 5, 1995, pp. 663–674.
22. Levine N., Kim K. E., and Nitz L. H. Spatial Analysis of Honolulu Motor Vehicle Crashes (II): Zonal Generators. Accident Analysis and Prevention, Vol. 27, No. 5, 1995, pp. 675–685.
23. Black W. R., and Thomas I. Accidents on Belgium's Motorways: A Network Autocorrelation Analysis. Journal of Transport Geography, Vol. 6, No. 1, 1998, pp. 23–31.
24. Nicholson A. Analysis of Spatial Distributions of Accidents. Safety Science, Vol. 31, 1999, pp. 71–91.
25. MacNab Y. C. Bayesian Spatial and Ecological Models for Small-Area Accident and Injury Analysis. Accident Analysis and Prevention, Vol. 36, No. 6, 2004, pp. 1028–1091.
26. 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, Vol. 37, No. 4, 2005, pp. 699–720.
27. Aguero-Valverde J., and Jovanis P. P. Spatial Analysis of Fatal and Injury Crashes in Pennsylvania. Accident Analysis and Prevention, Vol. 38, 2006, pp. 618–625.
28. Wang X., and Abdel-Aty M. Temporal and Spatial Analysis of Rear-End Crashes at Signalized Intersections. Accident Analysis and Prevention, Vol. 38, No. 6, 2006, pp. 1137–1150.
29. Kim D., Lee Y., Washington S., and Choi K. Modeling Crash Outcome Probabilities at Rural Intersections: Application of Hierarchical Binomial Logistic Models. Accident Analysis and Prevention, Vol. 39, No. 1, 2007, pp. 125–134.
30. Miaou S.-P., and Lord D. Modeling Traffic Crash–Flow Relationships for Intersections: Dispersion Parameter, Functional Form, and Bayes Versus Empirical Bayes Methods. In Transportation Research Record: Journal of the Transportation Research Board, No. 1840, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 31–40.
31. Brooks S. P., and Gelman A. Alternative Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics, Vol. 7, 1998, pp. 434–455.
32. Bedrick E. J., Christensen R., and Johnson W. A New Perspective on Priors for Generalized Linear Models. Journal of the American Statistical Association, Vol. 91, 1996, pp. 1450–1460.
33. Schluter P. J., Deely J. J., and Nicholson A. J. Ranking and Selecting Motor Vehicle Accident Sites by Using a Hierarchical Bayesian Model. Statistician, Vol. 46, No. 3, 1997, pp. 293–316.
34. 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, Vol. 131, No. 5, 2005, pp. 345–351.
35. Mitra S., and Washington S. On the Nature of Over-Dispersion in Motor Vehicle Crash Prediction Models. Accident Analysis and Prevention, Vol. 39, 2007, pp. 459–468.
36. 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, Vol. 40, 2008, pp. 1123–1134.
37. Miranda-Moreno L. F., Lord D., and Fu L. Evaluation of Alternative Hyper-Priors for Bayesian Road Safety Analysis. Presented at 87th Annual Meeting of the Transportation Research Board, Washington, D.C., 2008.
38. Breslow N. E., and Clayton D. G. Approximate Inference in Generalized Linear Mixed Models. Journal of the American Statistical Association, Vol. 88, No. 421, 1993, pp. 9–25.
39. Bernardinelli L., Clayton D., and Montomoli C. Bayesian Estimates of Disease Maps: How Important Are Priors. Statistics in Medicine, Vol. 14, 1995, pp. 2411–2431.
40. Spiegelhalter D., Thomas A., Best N., and Lunn D. WinBUGS User Manual. MRC Biostatistics Unit, Cambridge, United Kingdom, 2005.
41. 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, Vol. 64, 2002, pp. 1–34.
42. Sawalha Z., and Sayed T. Evaluating Safety of Urban Arterial Roadways. Journal of Transportation Engineering, Vol. 127, No. 2, 2001, pp. 151–158.
43. Qin X., Ivan J. N., and Ravishanker N. Selecting Exposure Measures in Crash Rate Prediction for Two-Lane Highway Segments. Accident Analysis and Prevention, Vol. 36, No. 2, 2004, pp. 183–191.
44. Lord D. The Prediction of Accidents on Digital Networks: Characteristics and Issues Related to the Application of Accident Prediction Models. PhD dissertation. University of Toronto, Toronto, Ontario, Canada, 2000.
45. Cheng W., and Washington S. P. Experimental Evaluation of Hotspot Identification Methods. Accident Analysis and Prevention, Vol. 37, 2005, pp. 870–881.
46. Shankar V., Milton J., and Mannering F. L. Modeling Accident Frequency as Zero-Altered Probability Processes: An Empirical Inquiry. Accident Analysis and Prevention, Vol. 29, No. 6, 1997, pp. 829–837.
47. Park E. S., and Lord D. Multivariate Poisson–Lognormal Models for Jointly Modeling Crash Frequency by Severity. In Transportation Research Record: Journal of the Transportation Research Board, No. 2019, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 1–6.
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