TA-55 Final Safety Analysis Report Comparison Document and DOE Safety Evaluation Report Requirements

Road safety analysis is typically undertaken using traffic collision data. However, the collision data often suffer from quality and reliability problems. These problems can inhibit the ability of road safety engineers to evaluate and analyze road safety performance. An alternate source of data that characterize the events of a traffic collision is the records that become available from an auto insurance claim. In settling an auto insurance claim, a claim adjuster must make an assessment and determination of the circumstances of the event, recording important contributing factors that led to the crash occurrence. As such, there is an opportunity to access and use the claims data in road safety engineering analysis. This paper presents the results of an initial attempt to use auto insurance claims records in road safety evaluation by developing and applying a claim prediction model. The prediction model will provide an estimate of the number of auto insurance claims that can be expected at signalized intersections in the Vancouver area of British Columbia, Canada. A discussion of the usefulness and application of the claim prediction model will be provided together with a recommendation on how the claims data could be utilized in the future.Key words: road safety improvement programs, auto insurance claims, road safety analysis, prediction models.

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Safety evaluation report for Morris Operation Fuel Storage Expansion, Phase I

10.2172/6570530 ◽

1975 ◽

Keyword(s):

Phase I ◽

Safety Evaluation ◽

Expansion Phase ◽

Evaluation Report ◽

Fuel Storage

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Probabilistic Safety Analysis of High Speed and Conventional Lines Using Bayesian Networks

Libro de Actas CIT2016. XII Congreso de Ingeniería del Transporte ◽

10.4995/cit2016.2016.3428 ◽

2016 ◽

Cited By ~ 1

Author(s):

Zacarias Grande Andrade ◽

Enrique Castillo Ron ◽

Alan O'Connor ◽

Maria Nogal

Keyword(s):

Bayesian Network ◽

High Speed ◽

Human Error ◽

Driver Behavior ◽

Safety Evaluation ◽

Safety Analysis ◽

Human Errors ◽

Maintenance Costs ◽

Railway Line ◽

Probabilistic Safety

A Bayesian network approach is presented for probabilistic safety analysis (PSA) of railway lines. The idea consists of identifying and reproducing all the elements that the train encounters when circulating along a railway line, such as light and speed limit signals, tunnel or viaduct entries or exits, cuttings and embankments, acoustic sounds received in the cabin, curves, switches, etc. In addition, since the human error is very relevant for safety evaluation, the automatic train protection (ATP) systems and the driver behavior and its time evolution are modelled and taken into account to determine the probabilities of human errors. The nodes of the Bayesian network, their links and the associated probability tables are automatically constructed based on the line data that need to be carefully given. The conditional probability tables are reproduced by closed formulas, which facilitate the modelling and the sensitivity analysis. A sorted list of the most dangerous elements in the line is obtained, which permits making decisions about the line safety and programming maintenance operations in order to optimize them and reduce the maintenance costs substantially. The proposed methodology is illustrated by its application to several cases that include real lines such as the Palencia-Santander and the Dublin-Belfast lines.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3428

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