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Journal of Advanced Transportation
Volume 2017, Article ID 1318945, 14 pages
https://doi.org/10.1155/2017/1318945
Research Article

Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

School of Civil Engineering, The University of Queensland, Brisbane, QLD, Australia

Correspondence should be addressed to Hamed Faroqi; ua.ude.qu@iqoraf.h

Received 30 April 2017; Revised 29 June 2017; Accepted 16 July 2017; Published 14 September 2017

Academic Editor: Zhi-Chun Li

Copyright © 2017 Hamed Faroqi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. J. Miller, M. Raubal, and Y. Jaegal, “Measuring space-time prism similarity through temporal profile curves,” in Proceedings of the In Geospatial Data in a Changing World, pp. 51–66, 2016.
  2. M.-P. Pelletier, M. Trépanier, and C. Morency, “Smart card data use in public transit: a literature review,” Transportation Research Part C: Emerging Technologies, vol. 19, no. 4, pp. 557–568, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Trépanier, C. Morency, and B. Agard, “Calculation of transit performance measures using smartcard data,” Journal of Public Transportation, vol. 12, no. 1, pp. 79–96, 2009. View at Publisher · View at Google Scholar
  4. B. Y. Chen and W. H. K. Lam, “Special issue: smart transportation: theory and practice,” Journal of Advanced Transportation, vol. 50, no. 2, pp. 141–144, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. Z.-J. Wang, X.-H. Li, and F. Chen, “Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data,” Transportation Research Part A: Policy and Practice, vol. 77, pp. 213–224, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Faroqi and A. Sadeghi-Niaraki, “GIS-based ride-sharing and DRT in Tehran city,” Public Transport, vol. 8, no. 2, pp. 243–260, 2016. View at Publisher · View at Google Scholar · View at Scopus
  7. Q. Li, Y. Zheng, X. Xie, Y. Chen, W. Liu, and W. Y. Ma, “Mining user similarity based on location history,” in Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, p. 34, 2008.
  8. L. Sun, K. W. Axhausen, D.-H. Lee, and X. Huang, “Understanding metropolitan patterns of daily encounters,” Proceedings of the National Academy of Sciences of the United States of America, vol. 110, no. 34, pp. 13774–13779, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Xu, H. Chen, Q. J. Kong, X. Zhai, and Y. Liu, “Urban traffic flow prediction: a spatio-temporal variable selection-based approach,” Journal of Advanced Transportation, vol. 50, no. 4, pp. 489–506, 2015. View at Google Scholar
  10. H. Nishiuchi, J. King, and T. Todoroki, “Spatial-temporal daily frequent trip pattern of public transport passengers using smart card data,” International Journal of Intelligent Transportation Systems Research, vol. 11, no. 1, pp. 1–10, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. X. Ma, Y. J. Wu, Y. Wang, F. Chen, and J. Liu, “Mining smart card data for transit riders travel patterns,” Transportation Research Part C: Emerging Technologies, vol. 36, no. Part C, pp. 1–12, 2013. View at Google Scholar
  12. S. Tao, J. Corcoran, I. Mateo-Babiano, and D. Rohde, “Exploring Bus Rapid Transit passenger travel behaviour using big data,” Applied Geography, vol. 53, pp. 90–104, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Tao, D. Rohde, and J. Corcoran, “Examining the spatial-temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap,” Journal of Transport Geography, vol. 41, pp. 21–36, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. L.-M. Kieu, A. Bhaskar, and E. Chung, “A modified density-based scanning algorithm with noise for spatial travel pattern analysis from smart card AFC data,” Transportation Research Part C: Emerging Technologies, vol. 58, pp. 193–207, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. M. S. Ghaemi, B. Agard, V. P. Nia, and M. Trépanier, “Challenges in spatial-temporal data analysis targeting public transport,” IFAC-PapersOnLine, vol. 48, no. 3, pp. 442–447, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. E. Manley, C. Zhong, and M. Batty, “Spatiotemporal variation in travel regularity through transit user profiling,” Transportation, pp. 1–30, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. X. Ma, C. Liu, H. Wen, Y. Wang, and Y. Wu, “Understanding commuting patterns using transit smart card data,” Journal of Transport Geography, vol. 58, pp. 135–145, 2017. View at Publisher · View at Google Scholar
  18. M. K. El Mahrsi, E. Côme, L. Oukhellou, and M. Verleysen, “Clustering Smart Card Data for Urban Mobility Analysis,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 3, 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. C. Yu and Z.-C. He, “Analysing the spatial-temporal characteristics of bus travel demand using the heat map,” Journal of Transport Geography, vol. 58, pp. 247–255, 2017. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Robinson, B. Narayanan, N. Toh, and F. Pereira, “Methods for pre-processing smartcard data to improve data quality,” Transportation Research Part C: Emerging Technologies, vol. 49, pp. 43–58, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Alsger, B. Assemi, M. Mesbah, and L. Ferreira, “Validating and improving public transport origin-destination estimation algorithm using smart card fare data,” Transportation Research Part C: Emerging Technologies, vol. 68, pp. 490–506, 2016. View at Publisher · View at Google Scholar · View at Scopus
  22. K. Bringmann, “Why walking the dog takes time: Frechet distance has no strongly subquadratic algorithms unless SETH fails,” in Proceedings of the 2014 IEEE 55th Annual Symposium, In Foundations of Computer Science (FOCS), pp. 661–670, October, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  23. C. Hu, N. Luo, and Q. Zhao, “Fast fuzzy trajectory clustering strategy based on data summarization and rough approximation,” Cluster Computing, vol. 19, no. 3, pp. 1411–1420, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Kim and H. S. Mahmassani, “Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories,” Transportation Research Procedia, vol. 9, pp. 164–184, 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Müller, “Information retrieval for music and motion,” Information Retrieval for Music and Motion, pp. 1–313, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. J. Shen and T. Cheng, “A framework for identifying activity groups from individual space-time profiles,” International Journal of Geographical Information Science, vol. 30, no. 9, pp. 1785–1805, 2016. View at Publisher · View at Google Scholar · View at Scopus
  27. H. Wang, H. Su, K. Zheng, S. Sadiq, and X. Zhou, “An effectiveness study on trajectory similarity measures,” in Proceedings of the In Proceedings of the Twenty-Fourth Australasian Database Conference-Volume 137, pp. 13–22, 2013.
  28. Y. Zheng, “Trajectory data mining: an overview,” ACM Transactions on Intelligent Systems and Technology, vol. 6, no. 3, article 29, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. D. A. Cunningham, P. A. Rechnitzer, M. E. Pearce, and A. P. Donner, “Determinants of self-selected walking pace across ages 19 to 66,” Journals of Gerontology, vol. 37, no. 5, pp. 560–564, 1982. View at Google Scholar · View at Scopus
  30. https://translink.com.au/about-translink/reports-and-publications.
  31. P. Sedgwick, “Pearson's correlation coefficient,” British Medical Journal, vol. 345, article e4483, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. N. Lewin-Koh, Hexagon binning: an overview. 2011. https://cran.r-project.org/web/packages/hexbin/vignettes/hexagon_binning.pdf.
  33. A. Alsger, A. Tavassoli, M. Mesbah, and L. Ferreira, “Evaluation of effects from sample-size origin-destination estimation using smart card fare data,” Journal of Transportation Engineering, vol. 143, no. 4, article 04017003, 2017. View at Publisher · View at Google Scholar · View at Scopus
  34. C. Chen, J. Ma, Y. Susilo, Y. Liu, and M. Wang, “The promises of big data and small data for travel behavior (aka human mobility) analysis,” Transportation Research Part C: Emerging Technologies, vol. 68, pp. 285–299, 2016. View at Publisher · View at Google Scholar · View at Scopus