Application of data clustering to railway delay pattern recognition

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

Documents

Links

DOI

View graph of relations

K-means clustering is employed to identify recurrent delay patterns on a high traffic railway line north of Copenhagen, Denmark. The clusters identify behavioral patterns in the very large (“big data”) data sets generated automatically and continuously by the railway signal system. The results reveal where corrective actions are necessary, showing where recurrent delay patterns take place. Delay profiles and delay-change profiles are generated from timestamps to compare different train runs, and to partition the set of observations into groups of similar elements. K-means clustering can identify and discriminate different patterns affecting the same stations, which is otherwise difficult in previous approaches based on visual inspection. Classical methods of univariate analysis do not reveal these patterns. The demonstrated methodology is scalable and can be applied to any system of transport.
Original languageEnglish
Article number6164534
JournalJournal of Advanced Transportation
Volume2018
Number of pages18
ISSN0197-6729
DOIs
Publication statusPublished - 2018
CitationsWeb of Science® Times Cited: No match on DOI
Download as:
Download as PDF
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
Word

Download statistics

No data available

ID: 146752425