Quantifying freight flow disruption risks from railroad accidents
Author:
Funder
U.S. Department of Transportation
Publisher
Springer Science and Business Media LLC
Subject
General Social Sciences,Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s11135-023-01727-3.pdf
Reference37 articles.
1. Agresti, A.: Statistical Methods for the Social Sciences. Pearson, Boston (2018)
2. Basallo-Triana, M.J., Bravo-Bastidas, J.J., Vidal-Holguín, C.J.: A rail-road transshipment yard picture. Transp. Res. Part E Log. Transp. Rev. 159, 102629 (2022). https://doi.org/10.1016/j.tre.2022.102629
3. Bešinović, N.: Resilience in railway transport systems: a literature review and research agenda. Transp. Rev. 40(4), 457 (2020). https://doi.org/10.1080/01441647.2020.1728419
4. Bhardwaj, B., Bridgelall, R., Pan, L., Dhingra, N.: Signal feature extraction and combination to enhance the detection and localization of railroad track irregularities. IEEE Sensors J. (2021). https://doi.org/10.1109/JSEN.2020.3041652
5. Bridgelall, R., Tolliver, D.D.: Railroad accident analysis using extreme gradient boosting. Accident Anal. Prevent. (2021). https://doi.org/10.1016/j.aap.2021.106126
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