Hotspot Identification: A Full Bayesian Hierarchical Modeling Approach

  • H.L. Huang
  • H.C. Chin
  • M.M. Haque


This study proposes a full Bayes (FB) hierarchical modeling approach in traffic crash hotspot identification. The FB approach is able to account for all uncertainties associated with crash risk and various risk factors by estimating a posterior distribution of the site safety on which various ranking criteria could be based. Moreover, by use of hierarchical model specification, FB approach is able to flexibly take into account various heterogeneities of crash occurrence due to spatiotemporal effects on traffic safety. Using Singapore intersection crash data (1997-2006), an empirical evaluate was conducted to compare the proposed FB approach to the state-of-the-art approaches. Results show that the Bayesian hierarchical models with accommodation for site specific effect and serial correlation have better goodness-of-fit than non-hierarchical models. Furthermore, all model-based approaches perform significantly better in safety ranking than the naive approach using raw crash count. The FB hierarchical models were found to significantly outperform the standard EB approach in correctly identifying hotspots.


Deviance Information Criterion Transportation Research Record Crash Risk Traffic Site Accident Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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We would like to thank Singapore Traffic Police and TSM Consultancy Pte Ltd for providing crash and geometric data of intersections used in this study.


  1. Brooks, S.P. and Gelman, A. (1998). Alternative methods for monitoring convergence of iterative simulations.Journal of Computational and Graphical Statistics , 7, 434-455.CrossRefGoogle Scholar
  2. Carlin, B. andLouis, T. (2000). Bayes and Empirical Bayes Methods for Data Analysis . Chapman and Hall.Google Scholar
  3. Chapman, R. (1973). The concept of exposure. Accident Analysis & Prevention , 5, 95-110.CrossRefGoogle Scholar
  4. Cheng, W. and Washington, S. (2005). Experimental evaluation of hotspot identification methods. Accident Analysis & Prevention , 37, 870–881.CrossRefGoogle Scholar
  5. Cheng, W. and Washington, S. (2008). New criteria for evaluating hotspot identification methods. The 87 th Annual Meeting of Transportation Research Board , Transportation Research Board, National Research Council, Washington D.C.Google Scholar
  6. Congdon, P. (2003). Applied Bayesian Modeling . John Wiley & Sons Ltd, England.CrossRefGoogle Scholar
  7. Deacon, J.A., Zegeer, C.V. and Deen, R.C. (1975). Identification of Hazardous Rural Highway Locations. Transportation Research Record , 543, 16-33.Google Scholar
  8. Elvik, R. (2007). State-of-the-art Approaches to Road Accident Black Spot Management and Safety Analysis of Road Networks . Report 883. Institute of Transport Economics, Oslo.Google Scholar
  9. Gelman, A., Carlin, J.B. and Stern, H.S. (2003). Bayesian Data Analysis, 2nd edition. Chapman & Hall, New York.Google Scholar
  10. Gilks, W.R., Richardson, S. and Spiegelhalter, D.J. (1995). Markov Chain Monte Carlo Methods in Practice . Chapman & Hall, New York.Google Scholar
  11. Hallmark, S.L., Basavaraju, R. and Pawlovich, M. (2002). Evaluation of the Iowa DOT’s Safety Improvement Candidate List Process. Final report, CTRE Project 00-74. Center for Transportation Research and Education, Iowa State University.Google Scholar
  12. Hakkert, A.S. and Mahalel, D. (1978). Estimating the number of accidents at intersections from a known traffic flow on the approaches. Accident Analysis and Prevention , 10, 69-79.CrossRefGoogle Scholar
  13. Hauer, E. (1980). Bias-by-selection: overestimation of the effectiveness of safety countermeasures caused by the process of selection for treatment. Accident Analysis and Prevention , 12, 113-117.CrossRefGoogle Scholar
  14. Hauer, E. (1996). Identification of sites with promise. Transportation Research Record , 1542, 54-60.CrossRefGoogle Scholar
  15. Hauer, E. (1997). Observational Before-After Studies in Road Safety . Pergamon /Elsevier Science, Inc., Tarrytown, New York.Google Scholar
  16. Hauer, E., Kononov, J., Allery, B. and Griffith, M.S. (2002). Screening the road network for sites with promise.Transportation Research Record , 1784, 27-32.CrossRefGoogle Scholar
  17. Hauer, E. and Persaud, B.N. (1984). Problem of identifying hazardous road locations using accident data. Transportation Research Record , 975, 36-43.Google Scholar
  18. Huang, H.L., Chin, H.C. and Haque, M.M. (2008). Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis, Accident Analysis & Prevention , 40, 45-54.CrossRefGoogle Scholar
  19. Huang, H.L., Chin, H.C. and Haque, M.M. (2008). Bayesian hierarchical analysis on crash prediction models. The 87 th Annual Meeting of Transportation Research Board , Transportation Research Board, National Research Council, Washington D.C.Google Scholar
  20. Layton, R.D. (1996). An Evaluation of the Safety Priority Index System . Traffic Management Section. Oregon Department of Transportation.Google Scholar
  21. Lord, D. and Miranda-Moreno, L.F. (2008). 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, 751-770.CrossRefGoogle Scholar
  22. Lord, D., Washington, S.P. and Ivan, J.N. (2005). Poisson, Poisson-gamma and zero-inflated regression models for motor vehicle crashes: balancing statistical fit and theory. Accident Analysis and Prevention , 37, 35-46.CrossRefGoogle Scholar
  23. Lord, D., Washington, S.P. and Ivan, J.N. (2007). Further notes on the application of zero-inflated models in highway safety. Accident Analysis and Prevention , 39, 53-57.CrossRefGoogle Scholar
  24. Maher, M.J. and Mountain, L.J. (1988). The identification of accident blackspots: a comparison of current methods. Accident Analysis & Prevention , 20, 143–151.CrossRefGoogle Scholar
  25. McGuigan, D.R.D. (1981). The use of relationships between road accidents and traffic flow in ‘black-spot’ identification. Traffic Engineering and Control , Aug.-Sept, 448-453.Google Scholar
  26. McGuigan, D.R.D. (1982). Nonjunction accident rates and their use in ‘black-spot’ identification. Traffic Engineering and Control, Feb, 45-56.Google Scholar
  27. Miaou, S.P. (1994). The relationship between truck accidents and geometric design of road section: Poisson versus negative binomial regression. Accident Analysis and Prevention , 26, 471-482.CrossRefGoogle Scholar
  28. Miaou, S.P. and Lord, D. (2003). Modeling traffic-flow relationships at signalized intersections: dispersion parameter, functional form and Bayes vs empirical Bayes. Transportation Research Record , 1840, 31-40.CrossRefGoogle Scholar
  29. Miaou, S.P. and Song, J.J. (2005). Bayesian ranking of sites for engineering safety improvement: decision parameter, treatability concept, statistical criterion and spatial dependence. Accident Analysis & Prevention, 37, 699–720.CrossRefGoogle Scholar
  30. Miranda-Moreno, L.F., Labbe, A. and Fu, L. (2007). Bayesian multiple testing procedures for hotspot identification. Accident Analysis & Prevention, 39, 1192-1201.CrossRefGoogle Scholar
  31. Morin, D.A. (1967). Application of statistical concepts to accident data. Highway Research Record, 188, 72-80.Google Scholar
  32. NCHRP (1986). Methods for Identifying Hazardous Highway Elements. National Cooperative Highway Research Program Report 128. Transportation Research Board, National Research Council, Washington D.C.Google Scholar
  33. Norden, M., Orlansky, J. and Jacobs, H. (1956). Application of Statistical Quality-control Techniques to Analysis of Highway-accident Data. Bulletin 117, HRB, National Research Council, Washington, D.C., 17-31.Google Scholar
  34. Persaud, B.N. (1999). Empirical Bayes procedure for ranking sites for safety investigation by potential for safety improvement. Transportation Research Record , 1665, 7-12.CrossRefGoogle Scholar
  35. Shankar, V.N., Mannering, F.L. and Barfield, W. (1995). Effect of roadway geometric and environmental factors on rural freeway accident frequencies. Accident Analysis and Prevention , 27, 371-389.CrossRefGoogle Scholar
  36. Song, J.J., Ghosh, M., Miaou, S. and Mallick, B. (2006). Bayesian multivariate spatial models for roadway traffic crash mapping. Journal of Multivariate Analysis , 97, 246-273.CrossRefGoogle Scholar
  37. Spiegelhalter, D.J., Best, N.G., Carlin, B.P. and Linde, V.D. (2003a). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society , B64(4), 583-616.Google Scholar
  38. Spiegelhalter, D.J., Thomas, A., Best, N.G. and Lunn, D. (2003b). WinBUGS version 1.4.1 User Manual . MRC Biostatistics Unit, Cambridge, UK.Google Scholar
  39. Stokes, R.W. and Mutabazi, I.M. (1996). Rate-quality control method of identifying hazardous road locations. Transportation Research Record , 1542, 44-48.CrossRefGoogle Scholar
  40. Tamburri, T.N. and Smith, R.N. (1970). The safety index: method of evaluating and rating safety benefits. Highway Research Record , 332, 28-39.Google Scholar
  41. Taylor, J.L. and Thompson, H.T. (1977). Identification of Hazardous Locations. Report No. FHWA-RD-77-81. Federal Highway Administration, Washington, D.C.Google Scholar
  42. Wang, X. and Abdel-Aty, M. (2006) Temporal and spatial analyses of rear-end crashes at signalized intersections. Accident Analysis and Prevention , 38, 1137-1150.CrossRefGoogle Scholar
  43. Yang, C. and MacNab (2003). A Bayesian hierarchical model for accident and injury surveillance. Accident Analysis & Prevention, 35, 91-102.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  • H.L. Huang
    • 1
  • H.C. Chin
    • 2
  • M.M. Haque
    • 2
  1. 1.University of Central FloridaCaliforniaU.S.A
  2. 2.National University of SingaporeSingaporeSingapore

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