Predicting Delay Occurrence at Freight Rail Sidings

Human dispatchers make freight rail dispatch decisions in real-time based on factors such as network traffic, network topology, and train characteristics. These decisions have significant influence on train delays and rail capacity, which motivates the development of tools to predict their effects. This article presents a machine learning framework to predict the occurrence of delay-inducing meets at sidings using an encoding of network state that incorporates information available to dispatchers at the time of prediction. Support vector classifiers (SVCs) are trained and predictions are compared to a simple deterministic baseline technique that uses only location information and treats trains equally. Testing is performed using historical data from a rail network in Tennessee, USA. Preliminary findings indicate that SVCs are able to exploit critical information beyond just train locations that is present in the network state to predict the occurrence of delays at sidings. The SVCs far outperform the baseline technique to which the authors compare and show that factors such as train length, train priority, and track occupancy have significant influence on delay occurrence.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AR030 Standing Committee on Railroad Operating Technologies.
  • Authors:
    • Martínez Mori, Juan Carlos
    • Barbour, William
    • Kuppa, Shankara
    • Work, Daniel B
  • Conference:
  • Date: 2018


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 17p

Subject/Index Terms

Filing Info

  • Accession Number: 01663958
  • Record Type: Publication
  • Report/Paper Numbers: 18-05352
  • Files: TRIS, TRB, ATRI
  • Created Date: Jan 8 2018 11:21AM