Canada has seen an increase in the amount of Dangerous Goods (DG) transported by rail by approximately 25% since 2004. Furthermore, Transport of Dangerous Goods (TDG) is forecasted to continue increasing. Sustainable growth in TDG by rail will require management of risk to acceptable safety levels. The first part of this study focuses on key occurrence types for TDG, incident causes, risk control strategies, and analysis of leading and lagging safety indicators. We also reviewed current safety performance and Canadian railway incident databases. Our results suggested that the performance against lagging indicators currently being reported is adequate, including derailments and collisions (main and non-main track), serious injuries (including fatalities), DG leakers, and releases. Also, a list of rail accidents with the greatest number of fatalities was used to calculate a crude estimate of societal risk associated with rail transportation. According to UK Health and Safety Executive recommendations, estimated rail transport risks can be considered acceptable when assessed at a milepost scale. However, there are opportunities for further enhancing safety reporting, management, and performance.
One potential area of improvement exists in the field of Safety Management Systems (SMS). The second task of this study focuses on enhancing railway SMS, particularly areas of SMS that might directly contribute to reducing the number of DG main-track train derailments. We applied detailed Root Cause Analyses (RCA), Bow Tie Analysis (BTA), and incident database analysis to identify the main causes and consequences of these types of accidents (2007-2017). The relationship between these factors and gaps in SMS elements were then identified and the frequency of each factor investigated. The results showed that the main gaps are related to process and equipment integrity, incident investigation, and company standards, codes, and regulations. We present some recommendations to improve the management of each SMS element and reduce these gaps.
In the last part of this study, we applied the Human Factors Analysis and Classification System (HFACS) approach to analyze 42 main-track derailments and collisions from 2007 to 2018 with the intention of studying the effect of human factors. Associations between adjacent sub-categories of the HFACS framework were analyzed to identify interdependencies between active and latent errors using chi-square tests and Kruskal’s lambda analysis. Furthermore, we implemented Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Network Process (ANP) methods to identify causal relationships between different sub-categories and calculated the weighted influence of each sub-category on main-track derailments and collisions. Finally, we compared this work and other studies that found relationships between active and latent errors in the railway industry. We found good agreement between the results of these studies, which highlights the importance of supervisory and organizational factors in the prevention of railway loss incidents. Based on these findings, we make several recommendations to reduce railway loss incidents.