01594440

Component

https://doi.org/10.3141/2546-15

Railroads support the national economy by carrying 43% of intercity freight ton-miles in the United States. At the same time, train accidents damage infrastructure and rolling stock, disrupt operations, and may result in casualties and environmental damage. While the majority of previous studies focused on the safety risks associated with train derailments or highway–rail grade-crossing incidents, much less work has been undertaken to evaluate train collision risk. This paper develops a statistical methodology for risk analysis of freight train collisions in the United States occurring between 2000 and 2014. Negative binomial regression models were developed to estimate the frequency of freight train collisions as a function of year and traffic volume, both by track type and accident cause. Overall, the rate of train collisions declined in the study period on both main line and yard tracks. Severity of train collisions, as measured by the average number of railcars derailed, varied with the type of track and accident cause. "Train collision risk," defined as the product of collision frequency and severity, is predicted for 2015 to 2017 on the basis of the safety trend in 2000 to 2014. The statistical procedures developed in this paper can be adapted to various other types of consequences, such as damage costs and casualties. Ultimately, this paper and its sequent studies aim to provide the railroad industry with data analytics tools by which to discover useful information from historical accidents so as to make risk-informed safety decisions.

01586598

16-1250

English

pp 121–128

2016

Transportation Research Record: Journal of the Transportation Research Board

9780309369947

Print

Figures (1) ; References (29) ; Tables (6)

Crash rates; Crash risk forecasting; Crash severity; Crashes; Freight trains; Regression analysis; Risk analysis; Trend (Statistics)

United States

Freight Transportation; Railroads; Safety and Human Factors

TRIS, TRB, ATRI

Jan 12 2016 4:33PM

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