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Forecasting estimated times of arrival of US freight trains

Author

Listed:
  • Zhen Liu
  • Qingsong Ma
  • Haichuan Tang
  • Jiebo Li
  • Ping Wang
  • Qing He

Abstract

Due to various reasons, variabilities in freight train travel time may be significant, yielding considerable challenges to forecasting the estimated time of arrival (ETA). Based on historical railway ETA data, this study first analyzes the shared route, then converts the historical data information of a train into multiple time data points, and finally, builds a tree-based model selection framework using the random forest algorithm (RF) and a feature weighted K-nearest neighbor algorithm (FWKNN) to create a phased prediction model. In terms of time, we study the use of different algorithms to predict the ETA of freight trains at various locations on freight train routes. In this study, the proposed method was tested on the dataset of the 2021 The Institute for Operations Research and the Management Sciences (INFORMS) Railway Applications Section (RAS) problem solving competition and won 2nd place.

Suggested Citation

  • Zhen Liu & Qingsong Ma & Haichuan Tang & Jiebo Li & Ping Wang & Qing He, 2022. "Forecasting estimated times of arrival of US freight trains," Transportation Planning and Technology, Taylor & Francis Journals, vol. 45(5), pages 427-448, July.
    • Handle: RePEc:taf:transp:v:45:y:2022:i:5:p:427-448
    DOI: 10.1080/03081060.2022.2115044
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