According to the Federal Railroad Administration (FRA) incident-accident database, from 2012 to 2017 there were 101 train handling accidents recorded by the on the mainlines of the four largest Class I railroads in the United States; from 2018 to 2023 there were 177 such train handling accidents, an increase of 75%. These two periods have also marked an increase in the length of freight trains operated in North America, and a decrease in train crew seniority and operating experience in the years following the COVID-19 pandemic, leading both trends to be cited as potential causing factors leading to the observed increase in train handling derailments
over time. However, since correlation does not imply causation, there is a need to further investigate these train handling derailments for possible shifts in their circumstances and locations over time. The nature of these geospatial and circumstantial changes may provide insight into the reasons for the increase in the frequency of train handling derailments, and help inform and optimize industry efforts to mitigate these incidents.
To address this research need, this project will use a combined statistical and geospatial analysis to determine if the location and sizes of trains involved in train handling accidents has changed between the two study periods of 2012-2017 and 2018-2023. Analysis of accident narratives and Geographic Information Systems (GIS) tools will be used to identify how train handling derailment hot spots have shifted between the two periods. Further clues regarding the changing frequency of train handling accidents could be the railroad track topography in the vicinity of the accident locations. Track features such as steep grades, undulating territory, and reverse curves could increase the risk of a train handling accident for longer trains or inexperienced crews. The proposed research will GIS use to overlay the North American Class I freight rail network onto elevation source data to estimate the grade and curvature of track segments in the vicinity of train handling derailments using sinuosity. The distribution of derailments by track topography will be compared to the overall composition of the network to identify if critical conditions are observed to change between the two study periods. Finally, the Advanced Locomotive Technology and Rail Infrastructure Optimization System (ALTRIOS) will be used to conduct train performance simulations over the track topography at each incident location to determine the throttle and/or brake settings leading up the incidents. The magnitude and number of changes in
those settings leading up to the accident location could infer the train handling demands placed on the train crew, and how those demands have changed between the two study periods.
The results from this research can inform freight railroads as to how the locations and circumstances of train handling accidents have changed as trains have become longer, enabling them to more effectively and efficiently deploy mitigation strategies. The proposed spatial analysis methodology could also be adapted and applied to investigate other railroad incident causes to improve overall rail network safety.