Examination of Methods to Estimate Crash Counts by Collision Type
Abstract
Multinomial logit (MNL) models have been applied extensively in transportation engineering, marketing, and recreational demand modeling. Thus far, this type of model has not been used to estimate the proportion of crashes by collision type. This study investigated the applicability of MNL models to predict the proportion of crashes by collision type and to estimate crash counts by collision type. MNL models were compared with two other methods described in recent publications to estimate crash counts by collision type: (a) fixed proportions of crash counts for all collision types and (b) collision type models. This study employed data collected between 2002 and 2006 on crashes that occurred on rural, two-lane, undivided highway segments in Minnesota. The study results showed that the MNL model could be used to predict the proportion of crashes by collision type, at least for the data set used. Furthermore, the method based on the MNL model was found useful to estimate crash counts by collision type, and it performed better than the method based on the use of fixed proportions. The use of collision type models, however, was still found to be the best way to estimate crash counts by specific collision type. In cases where collision type models are affected by the small sample size and a low sample-mean problem, the method based on the MNL model is recommended.
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References
1. Lord D., Geedipally S. R., Persaud B. N., Washington S. P., van Schalkwyk I., Ivan J. N., Lyon C., and Jonsson T. NCHRP Web-Only Document 126: Methodology to Predict the Safety Performance of Rural Multilane Highways. Transportation Research Board of the National Academies, Washington, D.C., 2008.
2. Ivan J. N., Wang C., and Bernardo N. R. Explaining Two-Lane Highway Crash Rates Using Land Use and Hourly Exposure. Accident Analysis and Prevention, Vol. 32, No. 6, 2000, pp. 787–795.
3. Lyon C., Oh J., Persaud B., Washington S., and Bared J. Empirical Investigation of Interactive Highway Safety Design Model Accident Prediction Algorithm: Rural Intersections. In Transportation Research Record: Journal of the Transportation Research Board, No. 1840, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 78–86.
4. Tarko A. P., Inerowicz M., Ramos J., and Li W. Tool with Road-Level Crash Prediction for Transportation Safety Planning. In Transportation Research Record: Journal of the Transportation Research Board, No. 2083, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 16–25.
5. 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.
6. Shankar V., Mannering F., and Barfield W. Effect of Roadway Geometric and Environmental Factors on Rural Freeway Accident Frequencies. Accident Analysis and Prevention, Vol. 27, No. 3, 1995, pp. 371–389.
7. Geedipally S. R., and Lord D. Investigating the Effect of Modeling Single-Vehicle and Multi-Vehicle Crashes Separately on Confidence Intervals of Poisson-Gamma Models. Accident Analysis and Prevention. Vol. 42, No. 4, 2010, pp. 1273–1282.
8. Abdel-Aty M., Keller J., and Brady P. A. Analysis of Types of Crashes at Signalized Intersections by Using Complete Crash Data and Tree-Based Regression. In Transportation Research Record: Journal of the Transportation Research Board, No. 1908, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 37–45.
9. Jonsson T., Lyon C., Ivan J. N., Washington S. P., van Schalkwyk I., and Lord D. Differences in the Performance of Safety Performance Functions Estimated for Total Crash Count and for Crash Count by Crash Type. In Transportation Research Record: Journal of the Transportation Research Board, No. 2102, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 115–123.
10. Kim D., Oh J., and Washington S. Modeling Crash Outcomes: New Insights into the Effects of Covariates on Crashes at Rural Intersections. ASCE Journal of Transportation Engineering, Vol. 132, No. 4, 2006, pp. 282–292.
11. 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.
12. Lord D., and Mannering F. The Statistical Analysis of Crash-Frequency Data: A Review and Assessment of Methodological Alternatives. Transportation Research Part A: Policy and Practice, Vol. 44. No. 5, pp. 291–305.
13. 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, Vol. 37, No. 1, 2005, pp. 35–46.
14. Shankar V., and Mannering F. An Exploratory Multinomial Logit Analysis of Single-Vehicle Motorcycle Accident Severity. Journal of Safety Research, Vol. 27, No. 3, 1996, pp. 183–194.
15. Carson J., and Mannering F. The Effect of Ice Warning Signs on Accident Frequencies and Severities. Accident Analysis and Prevention, Vol. 33, No. 1, 2001, pp. 99–109.
16. Abdel-Aty M. Analysis of Driver Injury Severity Levels at Multiple Locations Using Ordered Probit Models. Journal of Safety Research, Vol. 34, No. 5, 2003, pp. 597–603.
17. Kockelman K., and Kweon Y. J. Driver Injury Severity: An Application of Ordered Probit Models. Accident Analysis and Prevention, Vol. 34, No. 4, 2002, pp. 313–321.
18. 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.
19. Lord D., Guikema S. D., and Geedipally S. Application of the Conway-Maxwell-Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes. Accident Analysis and Prevention, Vol. 40, No. 3, 2008, pp. 1123–1134.
20. Jonsson T., Ivan J. N., and Zhang C. Crash Prediction Models for Intersections on Rural Multilane Highways: Differences by Collision Type. In Transportation Research Record: Journal of the Transportation Research Board, No. 2019, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 91–98.
21. McFadden D. Econometric Models of Probabilistic Choice. In Structural Analysis of Discrete Data with Econometric Applications (Manski, and McFadden D., eds.), MIT Press, Cambridge, Mass., 1981.
22. Koppelman F. S., and Bhat C. R. A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models. Prepared for the Federal Transit Administration, U.S. Department of Transportation, 2006, pp. 79–80.
23. Oh J., Lyon C., Washington S., Persaud B., and Bared J. Validation of FHWA Crash Models for Rural Intersections: Lessons Learned. In Transportation Research Record: Journal of the Transportation Research Board, No. 1840, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 41–49.
24. Greene W. H. LIMDEP, Version 9.0: User's Manual. Econometric Software, Plainview, N.Y., 2007.
25. SAS Institute Inc. Administrator Guide for the SAS System Version 9 for 64-bit Microsoft Windows. Cary, N.C., 2002.
26. Highway Safety Information System. FHWA, U.S. Department of Transportation. http://www.hsisinfo.org/.
27. Train K. E. Discrete Choice Methods with Simulation. Cambridge University Press, United Kingdom, 2003.
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Article first published online: January 1, 2010
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