Examining Rail Transportation Route of Crude Oil in the United States Using Crowdsourced Social Media Data

Yuandong Liu, Majbah Uddin, Shih Miao Chin, Ho Ling Hwang, Jiaoli Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Safety issues associated with transporting crude oil by rail have been a concern since the boom of the U.S. domestic shale oil production in 2012. During the last decade, over 300 crude-oil-by-rail incidents have occurred in the United States. Some of them have caused adverse consequences including fire and hazardous materials leakage. However, only limited information on crude-on-rail routes and their associated risks is available to the public. To this end, this study proposed an unconventional way to reconstruct crude-on-rail routes using geotagged photos harvested from the Flickr website. The proposed method linked the geotagged photos of crude oil trains posted online with national railway networks to identify potential railway segments that those crude oil trains were traveling on. A shortest path-based method was applied to infer the complete crude-on-rail routes, by utilizing the confirmed railway segments as well as their directional information. Validation of the inferred routes was performed using a public map and official crude oil incident data. The results suggested that the inferred routes based on geotagged photos had high coverage, with approximately 96% of the documented crude oil incidents aligned with the reconstructed crude-on-rail network. The inferred crude oil train routes were found to pass through several metropolitan areas of high population density, who were exposed to potential risk. These findings could improve situational awareness for policy makers and transportation planners. In addition, with the inferred routes, this study has established a good foundation for future crude oil train risk-analyses along the rail route.

Original languageEnglish
Pages (from-to)218-228
Number of pages11
JournalTransportation Research Record
Volume2678
Issue number1
DOIs
StatePublished - Jan 2024

Funding

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was sponsored by the U.S. Department of Energy under contract DE-AC05-00OR22725.

Keywords

  • GIS data and analysis
  • data and data science
  • freight big data
  • hazardous materials transportation
  • rail

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