Transportation: Identifying Hazmat Transportation Hazards and Potential Consequences

Identifying Hazmat Transportation Hazards and Potential Consequences

Recent work has focused on low-probability, high-consequence risk assessment and subsequent tasks will develop risk models that address overall hazmat transportation risk as well as mode-specific models.

FACTOR led our team’s technical risk assessment work in helping DOT’s Pipeline and Hazardous Materials Safety Administration’s (PHMSA) Safety Management System develop an approach to identify the most significant multi-modal hazmat transportation hazards and their potential consequences. The framework assessed the likelihood of incidents with different consequence severities, determined their relative risks, supported management of the risks through sound application of countermeasures to reduce probability or mitigate consequences, and systematically provides the means to monitor the ongoing performance.

We leveraged different hazmat incident databases in our approach, including extensive analysis of data within the PHMSA Hazardous Materials Incident Reporting System (HMIRS), FRA Rail Accident/Incident Reporting System (RAIRS), FMCSA Motor Carrier Management Information System (MCMIS), and the Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Substances Emergency Events Surveillance (HSEES).

High-Consequence Event Modeling

Modeling supports safety management systems and identifies high-consequence events for the Department of Transportation’s Pipeline and Hazardous Materials Safety Administration.

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Who We Work With

FACTOR worked with our consulting partners to deliver a comprehensive modeling and analysis report to PHMSA’s Office of Hazardous Materials Safety.

The tools we applied.

Fault Tree Modeling

This analysis examines potential failures that could lead to the top event. The elements at each level down from that top event is combined using Boolean logic (ANDs and ORs) and probabilities to determine the likelihood of the top event occurring based on the lower-level event occurring. Software products have been customized to support creating and analyzing fault trees.

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Statistical Modeling Software

When it comes to understanding relationships with complex data, particularly when the data represent only a sample of all possible data, researchers turn to statistical models. Such models can be used to test hypotheses, measure error and uncertainty, and inform decision makers.

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Advanced Risk Modeling

This refers to a range of techniques, usually quantitative, used to measure the risk of different, complex activities or situations. Advanced risk modeling can use innovative mathematical constructs, creative data creation, or sophisticated computational approaches.

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Root Cause Analysis Techniques

Root cause analysis (RCA) is a process used to determine the underlying factor(s) that led to a failure or other undesirable outcome. Insights from RCA can be used to help prevent future events, even in the absence of a failure. There are many tools and techniques available for performing RCA.

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Key Contributors

Emily Goodenough

Ravi Palakodeti

Richard Bornhorst

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