Quantitative causal analysis of mainline passenger train accidents in the United States

Chen-Yu Lin, M. Rapik Saat, Christopher Barkan

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


The need for shared freight and passenger rail corridors in the United States is increasing due to the growing demand for regional and intercity passenger transport. Several researches have been conducted on reducing the risk of freight train accidents, but little research has been done on the risk of passenger train accidents. The accident rates of passenger trains have declined in the past two decades; however, faster and more frequent passenger train services require even higher safety standards, and therefore further reduction to the risk of passenger train accidents is needed. The research presented in this paper analyzed the passenger train accidents in the United States using the Federal Railroad Administration train accident database to understand the trend of passenger train accident rates, the frequency and severity of different types of accidents, and to explore the major factors that cause them. Derailments and collisions were identified as the most significant types of passenger train accidents, and track failures and human factors, respectively, were the primary causes of those accidents. Accidents caused due to human factors and train operations such as train speed violations and failure to obey signals are often high-consequence accidents and therefore pose the greatest risk. Higher risk infrastructure-related factors include track geometry defects and broken rails or welds. This study on passenger train accidents provides a solid foundation for further research on improving the safety of passenger rail and shared-use rail corridors.
Original languageAmerican English
Pages (from-to)869-884
Number of pages15
JournalProceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Issue number8
StatePublished - 2020


  • Passenger rail safety
  • Train accident
  • train derailment
  • train collision
  • risk analysis
  • causal analysis


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