Automated Setting of Bus Schedule Coverage Using Unsupervised Machine Learning

Automated Setting of Bus Schedule Coverage Using Unsupervised Machine Learning

Khiari, J (NEC Laboratories Europe)
Moreira-Matias, L (NEC Laboratories Europe)
Cerqueira, Vitor (NEC Laboratories Europe)
Cats, O. (TU Delft Transport and Planning)

Bailey, J. (editor)
Khan, L. (editor)
Washio, T. (editor)
Dobbie, G. (editor)
Huang, J. (editor)
Wang, R. (editor)

2016

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.

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

http://resolver.tudelft.nl/uuid:b7ec5d96-f18f-4d3d-a845-a97f018423a1

https://doi.org/10.1007/978-3-319-31753-3_44

Springer

2018-02-01

978-3-319-31752-6

Advances in Knowledge Discovery and Data Mining, 9651

The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining, 2016-04-19 → 2016-04-22, Auckland, New Zealand

Lecture Notes in Computer Science (LNCS), 0302-9743, 9651

Institutional Repository

conference paper

© 2016 J Khiari, L Moreira-Matias, Vitor Cerqueira, O. Cats