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access icon free Understanding multiple days’ metro travel demand at aggregate level

  • Author(s): Zhengyu Duan 1 ; Zengxiang Lei 2 ; Michael Zhang 3 ; Haifeng Li 4 ; Dongyuan Yang 1
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  • Source: Volume 13, Issue 5, May 2019, p. 756 – 763
    DOI:  10.1049/iet-its.2018.5004 , Print ISSN 1751-956X, Online ISSN 1751-9578
© The Institution of Engineering and Technology
Received 31/01/2018, Accepted 06/12/2018, Revised 05/11/2018, Published 07/12/2018

Day-to-day variation of travel demand has been rarely studied, due to the limitation of traditional transport data collection methods and the difficulty in high-dimensional data processing. In this study, singular value decomposition (SVD) is used to study the day-to-day regularity of metro travel demand, based on four one-month datasets from the metro networks of Shanghai and Shenzhen, China. The results show that SVD is a tool to understand the intrinsic structure of daily metro travel demand. It is found that daily metro travel demand can be decomposed into three constituents: periodic part, burst part and other part. The periodic part varies weekly and accounts for a majority of the travel demand of origin–destination matrix. The burst part exhibits short-lived spikes, which are caused by special events or holidays. Also, other part varies randomly and only contributes a fraction of travel demand. Moreover, the periodic part corresponding to the two largest singular values is very stable in 2 months, and accounts for most of the travel demand. Finally, the burst part is used to analyse the impact of a collision accident. This work is helpful for short-term travel demand prediction, metro operation schedule and emergency management.

Inspec keywords: singular value decomposition; intelligent transportation systems; data analysis; traffic engineering computing

Other keywords: metro operation schedule; day-to-day variation; metro networks; singular value decomposition; intrinsic structure; daily metro travel demand; origin–destination matrix; day-to-day regularity; short-term travel demand prediction; aggregate level

Subjects: Other topics in statistics; Traffic engineering computing; Algebra; Data handling techniques

References

    1. 1)
      • 32. Yen, T.F., Reiter, M.K.: ‘Traffic aggregation for malware detection’. Int. Conf. on Detection of Intrusions and Malware, and Vulnerability Assessment, Paris, France, July 2008, pp. 207–227.
    2. 2)
      • 37. Verma, S., Narayanan, A., Zhang, Z.L.: ‘Multi-low-rank approximation for traffic matrices’. 29th Int. Teletraffic Congress, Genoa, Italy, September 2017, pp. 72–80.
    3. 3)
      • 20. Han, J., Yamana, H.: ‘A study on individual mobility patterns based on individuals’ familiarity to visited areas’, Int. J. Pervasive Comput. Commun., 2016, 12, (1), pp. 23–48.
    4. 4)
      • 23. Ramesh, V.: ‘Background modeling and subtraction of dynamic scenes’. Proc. Ninth IEEE Int. Conf. on Computer Vision, Nice, France, October 2003, pp. 1305–1312.
    5. 5)
      • 33. Callegari, C., Gazzarrini, L., Giordano, S., et al: ‘A novel PCA-based network anomaly detection’. IEEE Int. Conf. Communications, Kyoto, Japan, June 2011, pp. 1–5.
    6. 6)
      • 11. Bowman, J.L., Ben-Akiva, M.E.: ‘Activity-based disaggregate travel demand model system with activity schedules’, Transp. Res. A, Policy Pract., 2001, 35, (1), pp. 1–28.
    7. 7)
      • 34. Shenzhen Urban Planning & Land Resource Research Center: ‘The fifth comprehensive transportation survey report of Shenzhen’. Transport Commission of Shenzhen Municipality, Shenzhen, China, 2011.
    8. 8)
      • 12. Zhao, J., Rahbee, A., Wilson, N.H.: ‘Estimating a rail passenger trip origin destination matrix using automatic data collection systems’, Comput.-Aided Civil Infrastruct. Eng., 2007, 22, (5), pp. 376–387.
    9. 9)
      • 26. Lakhina, A., Papagiannaki, K., Crovella, M., et al: ‘Structural analysis of network traffic flows’, ACM Sigmetrics Perform. Eval. Rev., 2004, 32, (1), pp. 61–72.
    10. 10)
      • 15. Calabrese, F., Di Lorenzo, G., Liu, L., et al: ‘Estimating origin–destination flows using opportunistically collected mobile phone location data from one million users in Boston metropolitan area’, IEEE Pervasive Comput., 2011, 10, (4), pp. 36–44.
    11. 11)
      • 27. Zhu, Y., Li, Z., Zhu, H., et al: ‘A compressive sensing approach to urban traffic estimation with probe vehicles’, IEEE Trans. Mobile Comput., 2013, 12, (11), pp. 2289–2302.
    12. 12)
      • 29. Yang, C., Yan, F.F., Xu, X.D.: ‘Clustering daily metro origin–destination matrix in Shenzhen China’, Appl. Mech. Mater., 2015, 743, pp. 422–432.
    13. 13)
      • 16. Iqbal, M.S., Choudhury, C.F., Wang, P., et al: ‘Development of origin–destination matrices using mobile phone call data’, Transp. Res. C, Emerg. Technol., 2014, 40, pp. 63–74.
    14. 14)
      • 17. Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: ‘Understanding individual human mobility patterns’, Nature, 2008, 453, (7196), pp. 779–782.
    15. 15)
      • 2. Gärling, T., Eek, D., Loukopoulos, P., et al: ‘A conceptual analysis of the impact of travel demand management on private car use’, Transp. Policy, 2002, 9, (1), pp. 59–70.
    16. 16)
      • 6. McNally, M.G.: ‘The four-step model’, in Hensher, D.A., Button, K.J. (Ed.): ‘Handbook of transport modelling’ (Emerald Group Publishing Limited, Bingley, UK, 2007, 2nd edn.), pp. 35–53.
    17. 17)
      • 18. Song, C., Qu, Z., Blumm, N., et al: ‘Limits of predictability in human mobility’, Science, 2010, 327, (5968), pp. 1018–1021.
    18. 18)
      • 25. Bellegarda, J.R.: ‘Exploiting latent semantic information in statistical language modeling’. Proc. IEEE, 2000, 88, (8), pp. 1279–1296.
    19. 19)
      • 3. Xu, X., Chen, A., Yang, C.: ‘An optimization approach for deriving upper and lower bounds of transportation network vulnerability under simultaneous disruptions of multiple links’, Transp. Res. Proc., 2017, 23, pp. 645–663.
    20. 20)
      • 14. Dixon, M.P., Rilett, L.: ‘Population origin–destination estimation using automatic vehicle identification and volume data’, J. Transp. Eng., 2005, 131, (2), pp. 75–82.
    21. 21)
      • 8. Bhat, C.R., Koppelman, F.S.: ‘Activity-based modeling of travel demand’, in Hall, R.W. (Ed.): ‘Handbook of transportation Science’ (Springer, Boston, USA, 1999), pp. 35–61.
    22. 22)
      • 4. Bento, A.M., Cropper, M.L., Mobarak, A.M., et al: ‘The effects of urban spatial structure on travel demand in the United States’, Rev. Econ. Stat., 2005, 87, (3), pp. 466–478.
    23. 23)
      • 35. Duan, Z., Lei, Z., Zhang, M., et al: ‘Understanding evacuation and impact of a metro collision on ridership using large-scale mobile phone data’, IET Intell. Transp. Syst., 2017, 11, (8), pp. 511–520.
    24. 24)
      • 31. Soule, A., Lakhina, A., Taft, N., et al: ‘Traffic matrices: balancing measurements, inference and modeling’. Proc. ACM SIGMETRICS Int. Conf. on Measurement and Modeling of Computer System, Banff, Canada, June 2005, pp. 362–373.
    25. 25)
      • 5. Batty, M.: ‘Urban modeling’, in Kitchin, R., Thrift, N. (Ed.): ‘International encyclopedia of human geography’ (Elsevier, Oxford, UK, 2009), pp. 51–58.
    26. 26)
      • 21. Beijing Transport Research Center: ‘The fourth comprehensive transportation survey report of Beijing’ (Beijing Municipal Commission of Transport, Beijing, China, 2012).
    27. 27)
      • 13. Munizaga, M.A., Palma, C.: ‘Estimation of a disaggregate multimodal public transport origin–destination matrix from passive smartcard data from Santiago, Chile’, Transp. Res. C, Emerg. Technol., 2012, 24, pp. 9–18.
    28. 28)
      • 10. Pinjari, A.R., Bhat, C.R.: ‘Activity-based travel demand analysis’, in De Palma, A., Lindsey, R., Quinet, E., et al (Ed.): ‘A handbook of transport economics’ (Edward Elgar Publishing Limited, Cheltenham, UK, 2011), pp. 213–249.
    29. 29)
      • 38. Wang, Z., Hu, K., Yin, B. ‘An improved traffic matrix decomposition method with frequency-domain regularization’, IEICE Trans. Inf. Syst., 2013, 96, (3), pp. 731–734.
    30. 30)
      • 22. Nishiuchi, H., King, J., Todoroki, T.: ‘Spatial-temporal daily frequent trip pattern of public transport passengers using smart card data’, Int. J. Intell. Transp. Syst. Res., 2013, 11, (1), pp. 1–10.
    31. 31)
      • 7. Axhausen, K.W., Gärling, T.: ‘Activity-based approaches to travel analysis: conceptual frameworks, models, and research problems’, Transp. Rev., 1992, 12, (4), pp. 323–341.
    32. 32)
      • 19. Schneider, C.M., Belik, V., Couronné, T., et al: ‘Unravelling daily human mobility motifs’, J. R. Soc. Interface, 2013, 10, (84), pp. 1–8.
    33. 33)
      • 9. McNally, M.G., Rindt, C.R.: ‘The activity-based approach’, in Hensher, D.A., Button, K.J. (Ed.): ‘Handbook of transport modelling’ (Emerald Group Publishing Limited, Bingley, UK, 2007, 2nd edn.), pp. 55–73.
    34. 34)
      • 24. Deerwester, S., Dumais, S.T., Furnas, G.W., et al: ‘Indexing by latent semantic analysis’, J. Am. Soc. Inf. Sci., 1990, 41, (6), p. 391.
    35. 35)
      • 36. Candès, E.J., Li, X., Ma, Y., et al: ‘Robust principal component analysis?’, J. ACM, 2011, 58, (3), pp. 11:1–11:37.
    36. 36)
      • 1. Meyer, M.D., Miller, E.J.: ‘Urban transportation planning: a decision-oriented approach’ (McGraw-Hill Inc., New York, USA, 2001).
    37. 37)
      • 28. Zhi, Y., Li, H., Wang, D., et al: ‘Latent spatio-temporal activity structures: a new approach to inferring intra-urban functional regions via social media check-in data’, Geo Spat. Inf. Sci., 2016, 19, (2), pp. 94–105.
    38. 38)
      • 30. Sun, L., Axhausen, K.W.: ‘Understanding urban mobility patterns with a probabilistic tensor factorization framework’, Transp. Res. B, Methodol., 2016, 91, pp. 511–524.
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