Comprehensive risk assessment for rail transportation of dangerous goods: a validated platform for decision support

Currently, the most advanced and well documented risk assessments for the transportation of dangerous goods by railway take into account:(i) tics-based loss of containment frequencies, ication of potential consequences for a given release situations using event tree methodology as an organisational tool and uence calculation models to determine a risk figure known as CCDF (Complementary Cumulative Distribution Function). rocedures for the risk assessment (including for example decision-making on preventive measures) may offer only a limited insight into the causes and sequences leading to an accident and do not allow for any kind of predictive analysis. The present work introduces an enhanced solution, and a related software platform, which attempts to integrate loss of containment causes and consequences with systemʹs infrastructure and its environment. The solution features:(i) e of a detailed Master Logical Diagram, including fault/event tree analysis to determine a loss of containment frequency based on different initiating events, scenarios and specific basic data, aracterization of a resulting source term following a release situation, and lculation of various potential impacts on the neighbouring site. s are wrapped into a CCDF format for each selected traffic segment. The risk-related results are integrated on a software platform, structured as a decision support system using intelligent maps and a variety of GIS (Geographical Information System) data processing procedures. The introduction of the hot spot approach, allows us to focus on the most risk-relevant areas and to use information on various railway infrastructure elements (e.g. points, tunnels), are the basis of the new models employed. The software is applicable to any railway transportation system, comprising its technical infrastructure, rolling stock, human actions, regulation and management procedures. It provides the determination of the annual societal risk due to potential accident scenarios, while also revealing information on the potential causes of an accident taking into account spatial parameters. The approach and software have been validated by a case study done for a particular traffic segment of the Swiss Federal Railway company.