从数据挖掘到智能调度决策支持: 存在问题与实施路径

Translated title of the contribution: Closing the loop between data mining and fast decision support for intelligent train scheduling and traffic control

Research output: Contribution to journalArticleScientificpeer-review

21 Downloads (Pure)

Abstract

The existing Big Data of transport flows and railway operations can be mined through advanced statistical analysis and machine learning methods in order to describe and predict well the train speed, punctuality, track capacity and energy consumption. The accurate modelling of the real spatial and temporal distribution of line and network transport, traffic and performance stimulates a faster construction and implementation of robust and resilient timetables, as well as the development of efficient decision support tools for real-time rescheduling of train schedules. In combination with advanced train control and safety systems even (semi-) automatic piloting of trains on main and regional railway lines will become feasible in near future.

Original languageChinese
Pages (from-to)18-30
Number of pages13
JournalJournal of Beijing Jiaotong University. (Natural Science Edition)
Volume43
Issue number1
DOIs
Publication statusPublished - 2019

Keywords

  • Big railway data
  • Intelligent train rescheduling
  • Robust timetabling
  • Statistical learning
  • Train control

Fingerprint Dive into the research topics of 'Closing the loop between data mining and fast decision support for intelligent train scheduling and traffic control'. Together they form a unique fingerprint.

Cite this