Development of a Bayesian Network Based Accident Model for Hazmat Unit Trains

Johnson, Peter; Bailakanavar, Mahesh Raju

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Development of a Bayesian Network Based Accident Model for Hazmat Unit Trains

2023-09-01
By Johnson, Peter ; Bailakanavar, Mahesh Raju

File Language:

English

Details

Creators:

Johnson, Peter ; Bailakanavar, Mahesh Raju
Corporate Creators:

Thornton Tomasetti Inc
Corporate Contributors:

United States. Department of Transportation. Federal Railroad Administration. Office of Railroad Policy and Development ; United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology
Subject/TRT Terms:
Publication/ Report Number:

DOT/FRA/ORD-23/37
Resource Type:

Tech Report
Geographical Coverage:

United States
Contracting Officer:

Gonzalez, Francisco III
Corporate Publisher:

United States. Department of Transportation. Federal Railroad Administration
Abstract:

The objective of this research project was to develop a predictive risk model for the release of hazardous material transported in unit trains using data science techniques for available rail accident and traffic data. This work builds upon previous research (Bing, et al., 2015) which examined the causal sequence of events which can lead to a rail accident and used historical accident record and rail traffic data to define conditional probabilities of occurrence and thereby predict the risk of a hazmat release. Three different Bayesian Networks (BNs) were implemented to study the causal relationships between weather, track, and train related risk factors and the primary causes leading to railroad accidents. The BN-based accident model has demonstrated potential to accurately predict the accident cause, given information about the train and track.
Format:

PDF
Funding:

693JJ619C000016
Download URL:

https://rosap.ntl.bts.gov/view/dot/71913/dot_71913_DS1.pdf
File Type:

[PDF - 2.41 MB]
Collection(s):

Federal Railroad Administration
Main Document Checksum:

urn:sha-512:78da6194117f5288018c09f389879212b288756f94b0c6c96defaa679375bc620f5b470543900d43b0770da3ed28527c0faf1c5f015479428ea11e1b67c52a4a

File Language:

English

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