Mining smart card data for transit riders' travel patterns

Xiaolei Ma, Yao Jan Wu, Yinhai Wang, Feng Chen, Jianfeng Liu

Research output: Contribution to journalArticle

256 Scopus citations

Abstract

To mitigate the congestion caused by the ever increasing number of privately owned automobiles, public transit is highly promoted by transportation agencies worldwide. A better understanding of travel patterns and regularity at the "magnitude" level will enable transit authorities to evaluate the services they offer, adjust marketing strategies, retain loyal customers and improve overall transit performance. However, it is fairly challenging to identify travel patterns for individual transit riders in a large dataset. This paper proposes an efficient and effective data-mining procedure that models the travel patterns of transit riders in Beijing, China. Transit riders' trip chains are identified based on the temporal and spatial characteristics of their smart card transaction data. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm then analyzes the identified trip chains to detect transit riders' historical travel patterns and the K-Means++ clustering algorithm and the rough-set theory are jointly applied to cluster and classify travel pattern regularities. The performance of the rough-set-based algorithm is compared with those of other prevailing classification algorithms. The results indicate that the proposed rough-set-based algorithm outperforms other commonly used data-mining algorithms in terms of accuracy and efficiency.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalTransportation Research Part C: Emerging Technologies
Volume36
DOIs
StatePublished - Nov 2013

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Keywords

  • Automatic Fare Collection System
  • K-Means algorithm
  • Rough set theory
  • Smart card
  • Transit travel pattern

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

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