Identification and Ranking of Black Spots: Sensitivity Analysis
Abstract
In Flanders, Belgium, approximately 1,014 accident locations are considered dangerous. These dangerous accident sites, or black spots, are selected by means of historic accident records for the period 1997 to 1999. A combination of weighting values, respectively, 1 for each light injury, 3 for each serious injury, and 5 for each deadly injury (1_3_5), is used to rank and select the most dangerous accident locations. A sensitivity analysis was performed to investigate the effect on the identification and ranking of black spots as based on three different weighting value combinations, representing a different attitude toward the traffic safety problem: avoiding all accidents (1_1_1), all deadly accidents (1_1_10), and all accidents with serious or deadly injuries (1_10_10). Furthermore, effects of use of the expected number of accidents, estimated from a hierarchical Bayesian model, instead of the historic count data, to rank and select the accidents sites, were evaluated. Results show that a different attitude toward the traffic safety problem and the choice of the corresponding injury weighting values on the one hand and use of estimates instead of count values on the other hand do have important consequences for the selection and ranking of black spots. This is important not only for the number of accident locations that will receive a different ranking order but also for the effect on the type of accident locations that are selected as dangerous and accordingly for the resulting future traffic safety decisions.
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References
1. National Institute for Statistics. Jaarrapport Verkeersveiligheid 2000. Belgian Institute for Traffic Safety, Brussels, 2000.
2. Ministry of Flemish Community. Ontwerp-Mobiliteitsplan Vlaanderen, Brussels, Belgium. 2001. viwc.lin.vlaanderen.be/mobiliteit Accessed July 2003.
3. Dielemann L. Huidige Ontwikkelingen van het Verkeersveiligheidbeleid. Belgian Institute for Traffic Safety, Brussels, 2000.
4. Silcock D. T., and Smyth A. W. Methods of Identifying Accidents Blackspots. Transport Operations Research Group, Department of Civil Engineering, University of Newcastle upon Tyne, United Kingdom, 1985.
5. Maher M. A Bivariate Negative Binomial Model to Explain Traffic Accident Migration. Accident Analysis and Prevention, Vol. 22, No. 4, 1996, pp. 487–498.
6. Nguyen T. N. Identification of Accident Blackspot Locations: An Overview. VICRoads, Victoria, Australia, 1991.
7. Joly M.-F., Bourbeau R., Bergeron J., and Messier S. Analytical Approach to the Identification of Hazardous Road Locations: A Review of the Literature. Center for Transportation Research, University of Montreal, Quebec, Canada 1992.
8. Hauer E. Identification of Sites with Promise. In Transportation Research Record 1542, TRB, National Research Council, Washington D.C., 1996, pp. 54–60.
9. Thomas I. Spatial Data Aggregation: Exploratory Analysis of Road Accidents. Accident Analysis and Prevention, Vol. 28, 1996, pp. 251–264.
10. Flahaut B., Mouchart M., San Martin E., and Thomas I. The Local Spatial Autocorrelation and the Kernel Method for Identifying Black Zones. A Comparative Approach. Accident Analysis and Prevention, Vol. 35, No. 6, 2003, pp. 991–1004.
11. The Black Spot Program 1996-2002: An Evaluation of the First Three Years. Bureau of Transport and Regional Economics, Canberra, Australian Capital Territory, Australia, 2001.
12. Hauer E. On the Estimation of the Expected Number of Accidents. Accident Analysis and Prevention, Vol. 18, No. 1, 1986, pp. 1–12.
13. Elvik R. Evaluations of Road Accident Black Spot Treatment: A Case of the Iron Law of Evaluation Studies? Accident Analysis and Prevention, Vol. 29, No. 2, 1997, pp. 191–199.
14. Hauer E., and Persaud B. N. How to Estimate the Safety of Rail-Highway Grade Crossings and the Effects of Warning Devices. In Transportation Research Record 1114, TRB, National Research Council, Washington D.C., 1987, pp. 131–140.
15. Nassar S. Integrated Road Accident Risk Model, Ph.D. thesis. University of Waterloo, Ontario, Canada, 1996.
16. Geurts K., and Wets G. Black Spot Analysis Methods: Literature Review. Flemish Research Center for Traffic Safety, Diepenbeek, Belgium, 2003.
17. Brijs T., Karlis D., Van den Bossche F., and Wets G. A Bayesian Model for Ranking Hazardous Sites. Flemish Research Center for Traffic Safety, Diepenbeek, Belgium, 2003.
18. Saccomanno F. F., and Buyco C. Generalized Loglinear Models of Truck Accident Rates. In Transportation Research Record 1172, TRB, National Research Council, Washington D.C., 1988, pp. 23–31.
19. Blower D., Campbell K., and Green P. Accident Rates for Heavy Truck-Tractors in Michigan. Accident Analysis and Prevention, Vol. 25, No. 3, 1993, pp. 307–321.
20. Persaud B. Black Spot Identification and Treatment Evaluation. Research and Development Branch, Ministry of Transportation, Toronto, Ontario, Canada, 1990.
21. Miaou S. P. The Relationship Between Truck Accidents and Geometric Design of Road Sections: Poisson Versus Negative Binomial Regressions. Accident Analysis and Prevention, Vol. 26, No. 4, 1994, pp. 471–482.
22. Shankar V., Mannering F., and Barfield W. Effect of Roadway and Environmental Factors on Rural Freeway Accident Frequencies. Accident Analysis and Prevention, Vol. 27, No. 3, 1995, pp. 371–389.
23. Maher M. J., and Summersgill I. A Comprehensive Methodology for the Fitting of Predictive Accident Models. Accident Analysis and Prevention, Vol. 22, No. 5, 1996, pp. 487–498.
24. Hauer E. Observational Before-After Studies in Road Safety. Pergamon, Oxford, United Kingdom, 1997.
25. Abdel-Aty M. A., and Radwan A. E. Modelling Traffic Accident Occurrence and Involvement. Accident Analysis and Prevention, Vol. 32, No. 5, 2000, pp. 633–642.
26. Kulmala R. Safety at Rural Three- and Four-Arm Junctions. Technical Research Centre, Espoo, Finland, 1995.
27. Belanger C. Estimation of Safety of Four-Legged Unsignalized Intersections. In Transportation Research Record 1467, TRB, National Research Council, Washington D.C., 1994, pp. 23–29.
28. Vogelesang A. W. Bayesian Methods in Road Safety Research: An Overview. Institute for Road Safety Research, Leidschendam, Netherlands, 1996.
29. Goldstein H., and Spiegelhalter D. J. League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance. Journal of the Royal Statistical Society Series A, Vol. 159, 1996, pp. 385–443.
30. Schlüter 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, 1997, pp. 293–316.
31. Tunaru R. Hierarchical Bayesian Models for Multiple Count Data. Austrian Journal of Statistics, Vol. 31, 2002, pp. 221–229.
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Article first published: January 2004
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