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Public Transport Smart Card Data Analysis Using Tucker Decomposition

  • Mio HosoeEmail author
  • Masashi KuwanoEmail author
Conference paper
Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 12)

Abstract

With the development of information and communication technology, various kinds of data have been collected and accumulated in large quantities. Along with this, many methods for analyzing large-scale data have been proposed. This study extracts travel characteristics from smart card data of a private rail system using Tucker decomposition, a valid method for analyzing high order data. Applying Tucker decomposition to a 6th-order tensor dataset (weatherday of the weektime of daypassenger typeorigin stationdestination station), the results reveal what kind of users move from which station to which station, in what weather, on which days of the week, and at what time of day. Moreover, the dataset of 6,489,600 elements was compressed to 1,440 elements with the suggested approach, and the main travel patterns of passengers could be confirmed.

Keywords

Tensor factorization Smart card High order data Travel pattern 

Notes

Acknowledgements

The authors especially thank Takamastu-Kotohira Electric Railroad Co. in Takamatsu City, Kagawa Prefecture, Japan for providing smart card data.

References

  1. 1.
    Hofleitner, A., Herring, R., Bayen, A.: Arterial travel time forecast with streaming data: a hybrid approach of flow modeling and machine learning. Transp. Res. Part B 46, 1097–1122 (2012)CrossRefGoogle Scholar
  2. 2.
    Vij, A., Shankari, K.: When is big data enough? Implications of using GPS-based surveys for travel demand analysis. Transp. Res. Part C 56, 446–462 (2015)CrossRefGoogle Scholar
  3. 3.
    Simoncini, M., Taccari, L., Sambo, F., Bravi, L., Salti, S., Lori, A.: Vehicle classification from low-frequency GPS data with recurrent neural networks. Transp. Res. Part C 91, 176–191 (2018)CrossRefGoogle Scholar
  4. 4.
    Jenelius, E., Koutsopoulos, H.N.: Travel time estimation for urban road networks using low frequency probe vehicle data. Transp. Res. Part B 53, 64–81 (2013)CrossRefGoogle Scholar
  5. 5.
    Yang, H., Wang, Z., Xie, K., Dai, D.: Use of ubiquitous probe vehicle data for identifying secondary crashes. Transp. Res. Part C 82, 138–160 (2017)CrossRefGoogle Scholar
  6. 6.
    White, K.M., Hyde, M.K., Walsh, S.P., Watson, B.: Mobile phone use while driving: an investigation of the beliefs influencing drivers’ hands-free and hand-held mobile phone use. Transp. Res. Part F 13, 9–20 (2010)CrossRefGoogle Scholar
  7. 7.
    Jarv, O., Ahas, R., Witlox, F.: Understanding monthly variability in human activity spaces: a twelve-month study using mobile phone call detail records. Transp. Res. Part C 38, 122–135 (2014)CrossRefGoogle Scholar
  8. 8.
    Choudhary, P., Velaga, N.R.: Modelling driver distraction effects due to mobile phone use on reaction time. Transp. Res. Part C 77, 351–365 (2017)CrossRefGoogle Scholar
  9. 9.
    Ma, X., Wu, Y.J., Wang, Y., Chen, F., Liu, J.: Mining smart card data for transit riders’ travel patterns. Transp. Res. Part C 36, 1–12 (2013)CrossRefGoogle Scholar
  10. 10.
    Zhong, C., Manley, E., Arisona, S.M., Batty, M., Schmitt, G.: Measuring variability of mobility patterns from multiday smart-card data. J. Comput. Sci. 9, 125–130 (2015)CrossRefGoogle Scholar
  11. 11.
    Alsger, A., Tavassoli, A., Mesbah, M., Ferreira, L., Hickman, M.: Public transport trip purpose inference using smart card fare data. Transp. Res. Part C 87, 123–137 (2018)CrossRefGoogle Scholar
  12. 12.
    Wang, Z., Hu, Y., Zhu, P., Qin, Y., Jia, L.: Ring aggregation pattern of metro passenger trips: a study using smart card data. Phys. A 491, 471–479 (2018)CrossRefGoogle Scholar
  13. 13.
    Medina, S.A.O.: Inferring weekly primary activity patterns using public transport smatr card data and a household travel survey. Travel Behav. Soc. 12, 93–101 (2018)CrossRefGoogle Scholar
  14. 14.
    Zhang, Y., Martens, K., Long, Y.: Revealing group travel behavior patterns with public transit smart card data. Travel Behav. Soc. 10, 42–52 (2018)CrossRefGoogle Scholar
  15. 15.
    Zhao, Z., Koutsopoulos, H.N., Zhao, J.: Individual mobility prediction using transit smart card data. Transp. Res. Part C 89, 19–34 (2018)CrossRefGoogle Scholar
  16. 16.
    Simsekli, U., Virtanen, T., Cemgil, A.T.: Non-negative tensor factorization models for Bayesian audio processing. Digit. Sig. Proc. 47, 178–191 (2015)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Vazifehdan, M., Moattar, M.H., Jalali, M.: A hybrid Bayesian network and tensor factorization approach for missing value imputation to improve breast cancer recurrence prediction. J. King Saud Univ. Comput. Inf. Sci. 31, 175–184 (2018)Google Scholar
  18. 18.
    Fan, H., Kuang, G., Qiao, L.: Fast tensor principal component analysis via proximal alternating direction method with vectorized technique. Appl. Math. 8, 77–86 (2017)CrossRefGoogle Scholar
  19. 19.
    Taneja, A., Arora, A.: Cross domain recommendation using multidimensional tensor factorization. Expert Syst. Appl. 92, 304–316 (2018)CrossRefGoogle Scholar
  20. 20.
    Yao, D., Yu, C., Jin, H., Ding, Q.: Human mobility synthesis using matrix and tensor factorizations. Inf. Fusion 23, 25–32 (2015)CrossRefGoogle Scholar
  21. 21.
    Han, Y., Moutarde, F.: Analysis of large-scale traffic dynamics in an urban transportation network using non-negative tensor factorization. Int. J. Intell. Transp. Syst. Res. 14, 36–49 (2014)Google Scholar
  22. 22.
    Sun, L., Axhausen, K.: Understanding urban mobility patterns with a probabilistic tensor factorization framework. Transp. Res. Part B 91, 511–524 (2016)CrossRefGoogle Scholar
  23. 23.
    Chen, X., He, Z., Wang, J.: Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition. Transp. Res. Part C 86, 59–77 (2018)CrossRefGoogle Scholar
  24. 24.
    Feng, B., Lu, W., Sun, W., Huang, J., Shi, Y.Q.: Robust image watermarking based on tucker decomposition and adaptive-lattice quantization index modulation. Sig. Process. Image Commun. 41, 1–14 (2016)CrossRefGoogle Scholar
  25. 25.
    Wang, L., Bai, J., Wu, J., Jeon, G.: Hyperspectral image compression based on lapped transform and tucker decomposition. Sig. Process. Image Commun. 36, 63–69 (2015)CrossRefGoogle Scholar
  26. 26.
    Yin, W., Ma, Z.: LE & LLE regularized nonnegative tucker decomposition for clustering of high dimensional datasets. Neurocomputing 364, 77–94 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Management of Social Systems and Civil EngineeringTottori UniversityTottoriJapan

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