Statistical Analysis of Seasonal Effect on Freight Train Derailments
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 10
Abstract
Freight train accidents can damage infrastructure and rolling stock, disrupt operations, and possibly cause casualties and harm the environment. Understanding accident risks associated with major accident causes is an important step toward developing and prioritizing effective accident prevention strategies. This paper developed a negative binomial regression model to estimate freight-train derailment frequency on Class I railroad mainlines, accounting for derailment accident cause, traffic exposure, railroad, and season. The primary focus is to quantitatively measure the seasonal effect on freight-train derailment frequencies given traffic exposure. For model illustration, the analysis focused on three common derailment causes on freight railroads: broken rails, broken wheels, and track buckling, using the empirical Federal Railroad Administration (FRA)-reportable freight railroad derailment data on mainlines gathered between 2000 and 2016. The modeling results show that it tends to have high derailment rates in winter due to broken rails and broken wheels (double that of summer), whereas summer has the highest likelihood of buckling-caused derailment of all of the seasons (e.g., 6 times that of spring and 10 times that of fall). These analytical results can contribute to the risk-based optimization of rail and wheel inspection frequency. The statistical modeling methodology developed in this paper can be adapted to other types of train accidents or accident causes, ultimately supporting the optimal allocation of train safety improvement resources.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
A large portion of the research was completed when the lead author was a graduate research assistant at Rutgers University. We thank Rutgers University for the support of this work.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 10 • October 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Feb 19, 2021
Accepted: May 24, 2021
Published online: Aug 11, 2021
Published in print: Oct 1, 2021
Discussion open until: Jan 11, 2022
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Cited by
- Zhipeng Zhang, Chen-Yu Lin, Xiang Liu, Zheyong Bian, C Tyler Dick, Jiaxi Zhao, Steven Kirkpatrick, An Empirical analysis of freight train derailment rates for unit trains and manifest trains, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 10.1177/09544097221080615, 236, 10, (1168-1178), (2022).