Examining Rail Transportation Route of Crude Oil in the United States Using Crowdsourced Social Media Data
<|reference_start|>Examining Rail Transportation Route of Crude Oil in the United States Using Crowdsourced Social Media Data: Safety issues associated with transporting crude oil by rail have been a concern since the boom of US domestic shale oil production in 2012. During the last decade, over 300 crude oil by rail incidents have occurred in the US. Some of them have caused adverse consequences including fire and hazardous materials leakage. However, only limited information on the routes of crude-on-rail and their associated risks is available to the public. To this end, this study proposes an unconventional way to reconstruct the crude-on-rail routes using geotagged photos harvested from the Flickr website. The proposed method links the geotagged photos of crude oil trains posted online with national railway networks to identify potential railway segments those crude oil trains were traveling on. A shortest path-based method was then applied to infer the complete crude-on-rail routes, by utilizing the confirmed railway segments as well as their movement direction information. Validation of the inferred routes was performed using a public map and official crude oil incident data. Results suggested that the inferred routes based on geotagged photos have 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 many metropolitan areas with dense populations, who are exposed to potential risk. This finding could improve situation awareness for policymakers and transportation planners. In addition, with the inferred routes, this study establishes a good foundation for future crude oil train risk analysis along the rail route.<|reference_end|>
@article{liu2024examining,
    title={Examining Rail Transportation Route of Crude Oil in the United States
  Using Crowdsourced Social Media Data},
    author={Yuandong Liu, Majbah Uddin, Shih-Miao Chin, Ho-Ling Hwang, and Jiaoli
  Chen},
    journal={Transportation Research Record, 2678(1), 218-228 (2024)},
    year={2024},
    doi={10.1177/03611981231170632},
    archivePrefix={arXiv},
    eprint={2402.04020},
    primaryClass={cs.SI}
}