Pattern Detection For Large-Scale Railway Timetables

Please always quote using this URN: urn:nbn:de:0297-zib-63390
  • We consider railway timetables of our industrial partner DB Fernverkehr AG that operates the ICE high speed trains in the long-distance passenger railway network of Germany. Such a timetable covers a whole year with 364 days and, typically, includes more than 45,000 trips. A rolling stock rotation plan is not created for the whole timetable at once. Instead the timetable is divided into regular invariant sections and irregular deviations (e.g. for public holidays). A separate rotation plan with a weekly period can then be provided for each of the different sections of the timetable. We present an algorithmic approach to automatically recognize these sections. Together with the supplementing visualisation of the timetable this method has shown to be very relevant for our industrial partner.

Download full text files

Export metadata

  • Export Bibtex
Metadaten
Author:Stanley Schade, Ralf Borndörfer, Matthias Breuer, Boris Grimm, Markus Reuther, Thomas Schlechte, Patrick Siebeneicher
Document Type:ZIB-Report
Tag:pattern detection; railway timetables; visualization
MSC-Classification:90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
Date of first Publication:2017/11/04
Series (Serial Number):ZIB-Report (17-17)
ISSN:1438-0064
Published in:Appeared in: Proceedings of the IAROR conference RailLille 2017

$Rev: 13581 $