Assessing Casualty Risk of Railroad-Grade Crossing Crashes Using Zero-Inflated Poisson Models
Publication: Journal of Transportation Engineering
Volume 137, Issue 8
Abstract
A railroad grade crossing (RGC) is a spatial location where rail and highway users share the right-of-way. A significant number of traffic crashes and severe consequences at RGCs have signaled the need for appropriate models to investigate the key factors associated with the casualty risk level at an RGC in terms of the number of fatalities or injuries caused by one or more crashes in a specific time period. This study used a zero-inflated Poisson regression model to describe the relationship between the extra-zero count fatality or injury data and explanatory variables collected at 592 RGCs in Taiwan. The annual averaged daily traffic and the presence of Guidance Sign 31 were significantly associated with the probability of no fatality or injury encountered at an RGC; if an RGC was at risk of a fatality or injury, the number of daily trains, crossing angle, and Guidance Sign 31 significantly influenced the expected total number of fatalities or injuries caused by traffic crashes. The empirical results indicated that traffic exposure and traffic signage have significant effects on the risk levels of casualties at an RGC.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors are grateful for the insightful comments given by three anonymous reviewers, which significantly improve the clarity of the paper. This research was partially supported by grants UNSPECIFIEDNSC 95-2415-H-006-014-MY3 (S. R. Hu and C. K. Lee) from the National Science Council, Taiwan, and by Grant No. NSCTUL1 RR024146 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH)NIH, and NIH Roadmap for Medical Research (C. S. Li). We thank Vani Shanker, Ph.D., for her help in editing the manuscript and Matthew P. Smeltzer, M.S., for reading this manuscript.
References
Austin, R. D., and Carson, J. L. (2002). “An alternative crash prediction model for highway-rail interfaces.” Accid. Anal. Prev., 34(1), 31–42.
Ayyub, B. M. (2003). Risk analysis in engineering and economics, Chapman & Hall/CRC, New York.
Berg, W., Knoblauch, K., and Hucke, W. (1982). “Causal factors in railroad-highway grade crossing accidents.” Transp. Res. Rec., 847, 47–54.
Chang, S. H., Lee, C. K., Hu, S. R., and Tao, C. C. (2006). “A website and information platform for railroad grade crossing.” Technical Rep., Transportation and Communications Management Unit, Ministry of Transportation and Communications, Taiwan.
Dept. of Statistics. (2007). “Analysis of traffic incidents for the Taiwanese railway system.” Technical Rep., Ministry of Transportation and Communications, Taiwan.
Dietz, K., and Böhning, D. (1997). “The use of two-component mixture models with one completely or partly known component.” Comput. Stat., 12, 219–234.
Federal Highway Administration (FHA). (2000). “Manual on uniform traffic control devices.” U.S. DOT, Washington, DC.
Geurts, K., and Wets, G. (2003). Black spot analysis methods: Literature review, Technical Rep., Steunpunt Verkeersveiligheid.
Gitelman, V., and Hakkert, A. S. (1997). “The evaluation of road-rail crossing safety with limited crash statistics.” Accid. Anal. Prev., 29(2), 171–179.
Hopkins, J. B., and Hazel, M. E. (1971). “Technological innovations in grade crossing protection systems.” Rep. TSC-FRA-71-3, U.S. DOT, Washington, DC.
Int. Association of Traffic and Safety Sciences. (2007). “White paper on traffic safety in Japan 2007.” Directorate General for Policies on Cohesive Society, Cabinet Office, Japan.
Johnson, N. L., Kotz, S., and Kemp, A. W. (1992). Univariate discrete distributions, Wiley, New York.
Krove, H. W. (1999). “Traffic signal operation near highway-rail grade crossings.” NCHRP Synthesis 271, Transportation Research Board, National Research Council, Washington, DC.
Kulmala, R. (1995). “Safety at rural three- and four-arm junctions: Development and applications of accident prediction models.” VTT Publications 233, Technical Research Centre of Finland, Espoo.
Lambert, D. (1992). “Zero-inflated Poisson regression with an application to defects in manufacturing.” Technometrics, 34(1), 1–14.
Liao, T. F. (1994). “Interpreting probability models: Logit, Probit, and other generalized linear models.” Sage Univ. Paper Series on Quantitative Applications in the Social Sciences, Sage Univ., Newbury Park, CA.
Long, G. (2003). “Easy-to-apply solution to a persistent safety problem: clearance time for railroad-preempted traffic signals.” Transp. Res. Rec., 1856, 239–247.
Lord, D., Washington, S. P., and Ivan, J. N. (2005). “Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory.” Accid. Anal. Prev., 37(1), 35–46.
Lord, D., Washington, S. P., and Ivan, J. N. (2007). “Further notes on the application of zero-inflated models in highway safety.” Accid. Anal. Prev., 39(1), 53–57.
Mather, R. A. (1991). “Seven years of illumination at railroad-highway crossings.” Transp. Res. Rec., 1316, 54–57.
Mineta, N. Y. (2004). “Highway-rail crossing safety and trespass prevention.” Secretary’s Action Plan for Highway-Rail Crossing Safety and Trespass Prevention, U. S. DOT, Washington, DC.
Ministry of Transportation and Communications (MOTC). (1998). “A study on the planning of railway level crossing safety improvement in Taiwan.” Technical Rep., MOTC, Taiwan.
Miranda-Moreno, L. F., Fu, L., Saccomanno, F. F., and Labbe, A. (2005). “Alternative risk models for ranking locations for safety improvement.” Transp. Res. Rec., 1908, 1–8.
Nam, D., and Lee, J. (2006). “Crash frequency model using zero probability process.” Transp. Res. Rec., 1973, 142–148.
Office of Rail Regulation (ORR). (2006). “HMRI specific cost benefit analysis (CBA) checklist.” Technical Rep.,, ORR, London.
Ogden, B. D. (2007). “Railroad-highway grade crossing handbook: Revised second edition.” FHWA-SA-07-010, Federal Highway Administration, U.S. DOT, Washington, DC.
Oh, J., Washington, S., and Nam, D. (2006). “Accident prediction model for rail-way highway interfaces.” Accid. Anal. Prev., 38(2), 346–356.
Saccomanno, F. F., Fu, L., and Miranda-Moreno, L. F. (2004). “Risk-based model for identifying high-rail grade crossing blackspots.” Transp. Res. Rec., 1862, 127–135.
Schoppert, D. W., and Hoyt, D. W. (1968). “Factors influencing safety at highway-rail grade crossings.” NCHRP Rep. 50, Alan M. Voorhees and Associates, NAS-NRC, Washington, DC.
Schwarz, G. (1978). “Estimating the dimension of a model.” Ann. Stat., 6(2), 461–464.
Shankar, V., Milton, J., and Mannering, F. (1997). “Modeling accident frequencies as zero-altered probability processes: An empirical inquiry.” Accid. Anal. Prev., 29(6), 829–837.
Singh, S. (1963). “A note on inflated Poisson distribution.” Journal of the Indian Statistical Association, 1, 140–144.
Taiwan Railways Administration. (2007). “Crash statistics of the Taiwan Railways Administration system.” Technical Rep., Ministry of Transportation and Communications, Taiwan.
Transportation Safety Board of Canada. (2007). “Statistics of rail safety data.” Technical Rep., Quebec, Canada.
Vuong, Q. H. (1989). “Likelihood ratio tests for model selection and non-nested hypotheses.” Econometrica, 57(2), 307–333.
Washington, S., and Oh, J. (2006). “Bayesian methodology incorporating expert judgment for ranking countermeasures effectiveness under uncertainty: Example applied to at grade railroad crossings in Korea.” Accid. Anal. Prev., 38(2), 234–247.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 137 • Issue 8 • August 2011
Pages: 527 - 536
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Dec 24, 2009
Accepted: Oct 27, 2010
Published online: Nov 22, 2010
Published in print: Aug 1, 2011
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.