A modified Density-Based Scanning Algorithm with Noise for spatial travel pattern analysis from Smart Card AFC data

Kieu, Le Minh, Bhaskar, Ashish, & Chung, Edward (2015) A modified Density-Based Scanning Algorithm with Noise for spatial travel pattern analysis from Smart Card AFC data. Transportation Research Part C: Emerging Technologies, 58(Part B), pp. 193-207.

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Smart Card Automated Fare Collection (AFC) data has been extensively exploited to understand passenger behavior, passenger segment, trip purpose and improve transit planning through spatial travel pattern analysis. The literature has been evolving from simple to more sophisticated methods such as from aggregated to individual travel pattern analysis, and from stop-to-stop to flexible stop aggregation. However, the issue of high computing complexity has limited these methods in practical applications. This paper proposes a new algorithm named Weighted Stop Density Based Scanning Algorithm with Noise (WS-DBSCAN) based on the classical Density Based Scanning Algorithm with Noise (DBSCAN) algorithm to detect and update the daily changes in travel pattern. WS-DBSCAN converts the classical quadratic computation complexity DBSCAN to a problem of sub-quadratic complexity. The numerical experiment using the real AFC data in South East Queensland, Australia shows that the algorithm costs only 0.45% in computation time compared to the classical DBSCAN, but provides the same clustering results.

Impact and interest:

3 citations in Scopus
4 citations in Web of Science®
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ID Code: 82606
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Public Transport, Smart Card, AFC, Density-based Scanning Algorithm, Spatial travel pattern
DOI: 10.1016/j.trc.2015.03.033
ISSN: 0968-090X
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Transport Engineering (090507)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Research Centres > Smart Transport Research Centre
Copyright Owner: Copyright 2015 Elsevier
Copyright Statement: Licensed under the Creative Commons Attribution; Non-Commercial; No-Derivatives 4.0 International. DOI:
Deposited On: 19 Mar 2015 23:03
Last Modified: 24 Jun 2017 09:01

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