Conditional probability of release of hazardous materials from railroad tank cars using Bayesian networks
Date
2015
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Delaware
Abstract
Risk managers assessing hazardous materials release risk along various railroad routes
and regions are tasked with evaluating the average likelihood of hazmat release from a
derailed fleet of tank cars given varying proportions of tank car safety designs and
operating conditions. These variations or changes may be as a result of retrofitting or
phasing out of existing safety features (which have been deemed outmoded or
unacceptable in the new safety climate), tank car fleet upgrade, construction of tank
cars with new specifications, enhanced advanced braking rules or varying operating
speeds.
This thesis seeks to present Bayesian Networks (BNs) as a viable approach for
modelling and supporting decision making in the fields of hazardous materials
transportation risk and rail tank car safety. This approach estimates the average
Conditional Probability of Release (CPR) of an existing or projected fleet of cars
plying a given railroad route or region. CPR is one of the two primary components
used in the analysis of hazardous materials release risk.
This methodology can be used in assessing the reduction (or otherwise) of the average
CPR of an existing or proposed fleet of tanks cars given a change in risk reduction
option (tank car design safety feature or operating conditions). BNs allow for the
evaluation of the effect of new or alternate risk reduction options (RRO) on the total
network. They can also be used to evaluate the merits and demerits of the practice of
grandfathering from a release probability point of view.
Furthermore, Bayesian Networks can be used to easily rank the effect of various safety
features and operating conditions given a CPR estimate dataset of all possible state
combinations of the variables (risk reduction options) being considered. This allows
researchers and decision makers to make decisions on which RRO to employ. As a
result of interactive and flexible nature of BNs, these models can be integrated with
other models to arrive at such a decision. The resulting average CPR value obtained
from these models can be subsequently incorporated into the analysis of hazmat
transportation risk.
A CPR estimate dataset of possible combinations of four tank car design safety
features was considered in the study. The features were tank thickness, insulation,
head shield protection and top fittings protection. The aforementioned along with the
total CPR made up the random variables of the Bayesian Network. The BN modelling
was implemented using the commercially available HUGIN software. The average
CPRs of the tank cars were computed given varying proportions of risk reduction
options combinations. Sensitivity analysis was conducted to investigate the effect of
various risk reduction options on the CPR of the fleet which were subsequently
ranked.