How Complex Systems Sometimes Follow Murphy’s Law: Train Delay Prediction at a Station Using Delays at Previous Stops as the Features

Prediction or estimation of the delay of a train is essential to analyze customer behavior. In case of longer delays, there is a high probability that the customer will abort their travel or change the mode/time of travel. The delay propagation from previous stations to the next station creates a chain of these types of customer reactions. Thus, using delays at the previous station to predict the next station’s delay is a good approach to align with customer behavior. The present research attempts to predict the delays at a station using the delays at previous stations. The previous delays at stations are generated by creating lags in the original delays and creating them as one of the prediction features. The present research uses the delay data acquired from Bane NOR from 1 January 2021 to 28 February 2023. These data contain the scheduled and actual departure and arrival times of different trains between Oslo and Trondheim (up and down the line) in the specified period. The machine learning models based on neural networks were used on the data in the present research. Different prediction algorithms, i.e., recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM), were used. The prediction results are compared to look at the insights of the train delays in the given period. In conclusion, this study highlights how extreme feature engineering can negatively affect the output of a model.

• Record URL:
  • https://doi.org/10.1016/j.procs.2024.06.357
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1877050924016016
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770509
• Supplemental Notes:
  • © 2024 The Author(s). Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Mandhaniya, Pranjal
  • Olsson, Nils O E
  • Larsen, Anders S
  • Skjøren, Caroline
• Publication Date: 2024

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 1778-1783
• Serial:
  • Procedia Computer Science
  • Volume: 239
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0509
  • Serial URL: http://www.sciencedirect.com/science/journal/18770509
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Delays; Machine learning; Neural networks; Passenger trains; Passengers; Predictive models; Travel behavior
• Geographic Terms: Norway
• Subject Areas: Passenger Transportation; Planning and Forecasting; Railroads;

Filing Info

• Accession Number: 01927522
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 19 2024 7:36PM