Risk analysis of freight train collisions in the United States, 2000 to 2014

Yanlei Wang; Shuang Xu; Xiang Liu

doi:https://doi.org/10.1115/JRC2016-5738

Risk analysis of freight train collisions in the United States, 2000 to 2014

Yanlei Wang, Shuang Xu, Xiang Liu

School of Engineering, Civil & Environmental Engineering

Rutgers, The State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Train accidents damage infrastructure and rolling stock, disrupt operations, and may result in casualties and environmental damage. While the majority of previous studies focused on the safety risks associated with train derailments or highway-rail grade crossing collisions, much less work has been undertaken to evaluate train collision risk. This paper develops a statistical risk analysis methodology for freighttrain collisions in the United States between 2000 and 2014. Negative binomial regression models are developed to estimate the frequency of freight-train collisions as a function of year and traffic volume by accident cause. Train collision severity, measured by the average number of railcars derailed, varied with accident cause. Train collision risk, defined as the product of collision frequency and severity, is predicted for 2015 to 2017, based on the 2000 to 2014 safety trend. The statistical procedures developed in this paper can be adapted to various other types of consequences, such as damage costs or casualties. Ultimately, this paper and its sequent studies aim to provide the railroad industry with data analytic tools to discover useful information from historical accidents so as to make risk-informed safety decisions.

Original language	English (US)
Title of host publication	2016 Joint Rail Conference, JRC 2016
Publisher	American Society of Mechanical Engineers
ISBN (Electronic)	9780791849675
DOIs	https://doi.org/10.1115/JRC2016-5738
State	Published - 2016
Event	2016 Joint Rail Conference, JRC 2016 - Columbia, United States Duration: Apr 12 2016 → Apr 15 2016

Publication series

Name	2016 Joint Rail Conference, JRC 2016

Other

Other	2016 Joint Rail Conference, JRC 2016
Country/Territory	United States
City	Columbia
Period	4/12/16 → 4/15/16

ASJC Scopus subject areas

Transportation

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https://doi.org/10.1115/JRC2016-5738

Cite this

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title = "Risk analysis of freight train collisions in the United States, 2000 to 2014",

abstract = "Train accidents damage infrastructure and rolling stock, disrupt operations, and may result in casualties and environmental damage. While the majority of previous studies focused on the safety risks associated with train derailments or highway-rail grade crossing collisions, much less work has been undertaken to evaluate train collision risk. This paper develops a statistical risk analysis methodology for freighttrain collisions in the United States between 2000 and 2014. Negative binomial regression models are developed to estimate the frequency of freight-train collisions as a function of year and traffic volume by accident cause. Train collision severity, measured by the average number of railcars derailed, varied with accident cause. Train collision risk, defined as the product of collision frequency and severity, is predicted for 2015 to 2017, based on the 2000 to 2014 safety trend. The statistical procedures developed in this paper can be adapted to various other types of consequences, such as damage costs or casualties. Ultimately, this paper and its sequent studies aim to provide the railroad industry with data analytic tools to discover useful information from historical accidents so as to make risk-informed safety decisions.",

author = "Yanlei Wang and Shuang Xu and Xiang Liu",

note = "Publisher Copyright: Copyright {\textcopyright} 2016 by ASME.; 2016 Joint Rail Conference, JRC 2016 ; Conference date: 12-04-2016 Through 15-04-2016",

year = "2016",

doi = "https://doi.org/10.1115/JRC2016-5738",

language = "English (US)",

series = "2016 Joint Rail Conference, JRC 2016",

publisher = "American Society of Mechanical Engineers",

booktitle = "2016 Joint Rail Conference, JRC 2016",

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T1 - Risk analysis of freight train collisions in the United States, 2000 to 2014

AU - Wang, Yanlei

AU - Xu, Shuang

AU - Liu, Xiang

PY - 2016

Y1 - 2016

N2 - Train accidents damage infrastructure and rolling stock, disrupt operations, and may result in casualties and environmental damage. While the majority of previous studies focused on the safety risks associated with train derailments or highway-rail grade crossing collisions, much less work has been undertaken to evaluate train collision risk. This paper develops a statistical risk analysis methodology for freighttrain collisions in the United States between 2000 and 2014. Negative binomial regression models are developed to estimate the frequency of freight-train collisions as a function of year and traffic volume by accident cause. Train collision severity, measured by the average number of railcars derailed, varied with accident cause. Train collision risk, defined as the product of collision frequency and severity, is predicted for 2015 to 2017, based on the 2000 to 2014 safety trend. The statistical procedures developed in this paper can be adapted to various other types of consequences, such as damage costs or casualties. Ultimately, this paper and its sequent studies aim to provide the railroad industry with data analytic tools to discover useful information from historical accidents so as to make risk-informed safety decisions.

AB - Train accidents damage infrastructure and rolling stock, disrupt operations, and may result in casualties and environmental damage. While the majority of previous studies focused on the safety risks associated with train derailments or highway-rail grade crossing collisions, much less work has been undertaken to evaluate train collision risk. This paper develops a statistical risk analysis methodology for freighttrain collisions in the United States between 2000 and 2014. Negative binomial regression models are developed to estimate the frequency of freight-train collisions as a function of year and traffic volume by accident cause. Train collision severity, measured by the average number of railcars derailed, varied with accident cause. Train collision risk, defined as the product of collision frequency and severity, is predicted for 2015 to 2017, based on the 2000 to 2014 safety trend. The statistical procedures developed in this paper can be adapted to various other types of consequences, such as damage costs or casualties. Ultimately, this paper and its sequent studies aim to provide the railroad industry with data analytic tools to discover useful information from historical accidents so as to make risk-informed safety decisions.

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M3 - Conference contribution

T3 - 2016 Joint Rail Conference, JRC 2016

BT - 2016 Joint Rail Conference, JRC 2016

PB - American Society of Mechanical Engineers

T2 - 2016 Joint Rail Conference, JRC 2016

Y2 - 12 April 2016 through 15 April 2016

ER -

Risk analysis of freight train collisions in the United States, 2000 to 2014

Abstract

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