Dwell time estimation using real-time train operation and smart card-based passenger data: A case study in seoul, south korea

Yoonseok Oh, Young Ji Byon, Ji Young Song, Ho Chan Kwak, Seungmo Kang

Research output: Contribution to journalArticle


Dwell time is a critical factor in constructing and adjusting railway timetables for efficient and accurate operation of railways. This paper develops dwell time estimation models for a Shinbundang line (S line) in Seoul, South Korea using support vector regression (SVR), multiple linear regression (MLR), and random forest (RF) techniques utilizing archived real-time metro operation data along with smart card-based passenger information. In the first phase of this research, the collected data are processed to extract boarding and alighting passenger counts and observed dwell times of each train at all stations of the S line under the current operational environment. In the second phase, we develop SVR, MLR, and RF-based dwell time estimation models. It is found that the SVR-based model successfully estimates the dwell times within 10 s of differences for 84.4% of observed data. The results of this paper are especially beneficial for autonomous railway operations that need constructing and maintaining dynamic railway timetables that require reliable dwell time predictions in real-time.

Original languageEnglish
Article number476
JournalApplied Sciences (Switzerland)
Issue number2
Publication statusPublished - 2020 Jan 1


  • Artificial intelligence
  • Dwell time estimation
  • Metro timetable
  • Railway operation data
  • Smart card
  • Transit ridership

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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