Analysis of train derailments and collisions to identify leading causes of loss incidents in rail transport of dangerous goods in Canada

Abstract

The Canadian railway industry has improved safety performance in the last decade as measured by freight loss incidents per billion gross ton-miles. Further improvements in safety performance require a deeper analysis of the leading causes to identify weaknesses in implementing safety systems. In this paper, we classify the causes of railway loss incidents using a Safety Management System (SMS) framework to identify system weaknesses. The role of human factors is further analyzed through the Human Factors Analysis and Classification System (HFACS) approach. For this, we utilized data from 42 main track derailments and collisions involving the transport of dangerous goods in Canada between 2007 and 2018, which have been investigated by the Transportation Safety Board of Canada in detail. Associations between adjacent sub-categories of the HFACS framework are analyzed to identify any interdependency that exists between active and latent errors using a Chi-square test and Kruskal's lambda analysis. Furthermore, we implement the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and the Analytical Network Process (ANP) to identify causal relationships between different sub-categories of the HFACS framework and calculate the weighted influence of each sub-category on main track derailments and collisions. Finally, a comparison is made between this work and others', which have analyzed human factors in the railway industry. There is good agreement between the results of these studies that highlight the importance of supervisory and organizational factors in the prevention of railway loss incidents. Based on these findings, we make recommendations to reduce railway loss incidents.