A connected driver advisory system framework for merging freight trains

Pengling Wang, Rob M.P. Goverde, Jelle van Luipen

Research output: Contribution to journalArticleScientificpeer-review

Abstract

This paper proposes an approach to facilitate smooth merging of freight trains into a stream of passenger trains with short headways, to help drivers better control freight trains and avoid red signals. An algorithm architecture is proposed for Driver Advisory Systems (DASs) to compute time/speed advice for freight train drivers. The framework includes four parts: buffer stairway prediction, freight train movement prediction, merging window detection and merging optimization. The basic idea is to predict the traffic state in the merging area regularly and find the feasible merging time window. Proper advice can be presented to freight train drivers and help them to merge smoothly, by comparing the freight train movement to the feasible merging window. The performance of the proposed algorithms is illustrated on examples of merging freight trains in the Meteren and Kijfhoek areas on the Dutch railway network. The experimental results show the efficiency and quality of the proposed algorithms on real world size problems.
Original languageEnglish
Pages (from-to)203-221
Number of pages19
JournalTransportation Research Part C: Emerging Technologies
Volume105
DOIs
Publication statusPublished - 2019

Keywords

  • Driver advisory system
  • Freight train transport
  • Optimization
  • Train traffic prediction

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