Automated Setting of Bus Schedule Coverage Using Unsupervised Machine Learning

J Khiari, L Moreira-Matias, Vitor Cerqueira, Oded Cats

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

11 Citations (Scopus)
27 Downloads (Pure)

Abstract

The efficiency of Public Transportation (PT) Networks is a major goal of any urban area authority. Advances on both location and communication devices drastically increased the availability of the data generated by their operations. Adequate Machine Learning methods can thus be applied to identify patterns useful to improve the Schedule Plan. In this paper, the authors propose a fully automated learning framework to determine the best Schedule Coverage to be assigned to a given PT network based on Automatic Vehicle location (AVL) and Automatic Passenger Counting (APC) data. We formulate this problem as a clustering one, where the best number of clusters is selected through an ad-hoc metric. This metric takes into account multiple domain constraints, computed using Sequence Mining and Probabilistic Reasoning. A case study from a large operator in Sweden was selected to validate our methodology. Experimental results suggest necessary changes on the Schedule coverage. Moreover, an impact study was conducted through a large-scale simulation over the affected time period. Its results uncovered potential improvements of the schedule reliability on a large scale.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
EditorsJ. Bailey, L. Khan, T. Washio, G. Dobbie, J. Huang, R. Wang
PublisherSpringer
Pages552-564
Number of pages13
Volume9651
ISBN (Electronic)978-3-319-31753-3
ISBN (Print)978-3-319-31752-6
DOIs
Publication statusPublished - 2016
EventThe 20th Pacific Asia Conference on Knowledge Discovery and Data Mining - Auckland, New Zealand
Duration: 19 Apr 201622 Apr 2016
Conference number: 20

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer
Volume9651
ISSN (Electronic)0302-9743

Conference

ConferenceThe 20th Pacific Asia Conference on Knowledge Discovery and Data Mining
Abbreviated titlePAKDD 2016
CountryNew Zealand
CityAuckland
Period19/04/1622/04/16

Keywords

  • Unsupervised learning
  • Public transportation
  • Big data
  • Schedule plan
  • Schedule coverage
  • Sequence mining
  • Probabilistic reasoning

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