single-dr.php

JDR Vol.12 No.2 pp. 287-295
(2017)
doi: 10.20965/jdr.2017.p0287

Paper:

Predicting Delay of Commuting Activities Following Frequently Occurring Disasters Using Location Data from Smartphones

Takahiro Yabe*,†, Yoshihide Sekimoto*, Akihito Sudo*, and Kota Tsubouchi**

*The University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo, Japan

Corresponding author

**Yahoo Japan Corporation, Tokyo, Japan

Received:
October 31, 2016
Accepted:
March 7, 2017
Online released:
March 16, 2017
Published:
March 20, 2017
Keywords:
urban dynamics, frequent disasters, GPS data, machine learning, commuting activities
Abstract

Natural disasters that frequently occur, such as typhoons and earthquakes, heavily affect human activities in urban areas by causing severe congestion and economic loss. Predicting the delay in usual commuting activities of individuals following such disasters is crucial for managing urban systems. We propose a novel method that predicts such delay of individuals’ movements in several frequently occurring disasters using various types of features including the commuters’ usual movement patterns, disaster information, and geospatial information of commuters’ locations. Our method predicts the irregularity of commuting activities in metropolitan Tokyo during several typhoons, and earthquakes, using Yahoo Japan’s GPS dataset of 1 million users. The results show that the irregularity of individuals’ movements are significantly more predictable than with previous models. Also, we are able to understand that commuters’ usual movement patterns, disaster intensity, and geospatial features including road density and population density are main factors that cause commuting delay following disasters.

Cite this article as:
T. Yabe, Y. Sekimoto, A. Sudo, and K. Tsubouchi, “Predicting Delay of Commuting Activities Following Frequently Occurring Disasters Using Location Data from Smartphones,” J. Disaster Res., Vol.12, No.2, pp. 287-295, 2017.
Data files:
References
  1. [1] United Nations System Task Team on the POST-2015 UN Development Agenda, “Disaster Risk and Resilience,” 2012.
  2. [2] Japanese Government Cabinet Office, “Final Report of Council for Stranded Commuters in Tokyo Metropolitan Earthquake,” 2012(in Japanese).
  3. [3] N. Okada, T. Ye, Y. Kajitani, P. Shi, and H. Tatano, “The 2011 eastern Japan great earthquake disaster: Overview and comments.” Int. Journal of Disaster Risk Science, Vol.2, No.1, pp. 34-42, 2011.
  4. [4] X. Lu, L. Bengtsson, and P. Holme, “Predictability of population displacement after the 2010 Haiti earthquake,” Proc. National Academy of Sciences, Vol.109, No.29, pp. 11576-11581, 2012.
  5. [5] X. Song, Q. Zhang, Y. Sekimoto, T. Horanont, S. Ueyama, and R. Shibasaki, “Modeling and probabilistic reasoning of population evacuation during large-scale disaster,” Proc. ACM SIGKDD, 2013.
  6. [6] Q. Wang and J. E. Taylor, “Quantifying human mobility perturbation and resilience in Hurricane Sandy,” PLoS one, Vol.9, No.11, 2014.
  7. [7] C. M. Schneider, V. Belik, T. Couronné, Z. Smoreda, and M. C. González, “Unravelling daily human mobility motifs,” Journal of The Royal Society Interface, Vol.10, No.84, 2013.
  8. [8] Y. Sekimoto, R. Shibasaki, H. Kanasugi, T. Usui, and Y. Shimazaki, “Pflow: Reconstructing people flow recycling large-scale social survey data,” IEEE Pervasive Computing, Vol.10, No.4, 2011.
  9. [9] X. Shao, “Tracking a Variable No.of Pedestrians in Crowded Scenes by using Laser Range Scanners,” IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 1545-1551, 2008.
  10. [10] D. B. Yang, H. H. González-Baños, and L. J. Guibas, “Counting people in crowds with a real-time network of simple image sensors,” Computer Vision, Proc. IEEE, 2003.
  11. [11] D. Ashbrook and T. Starner, “Using GPS to learn significant locations and predict movement across multiple users,” Personal and Ubiquitous Computing, Vol.7, No.5, pp. 275-286, 2003.
  12. [12] F. Calabrese, Di G. Lorenzo, L. Liu, and C. Ratti, “Estimating origin-destination flows using mobile phone location data,” IEEE Pervasive Computing, Vol.10, No.4, 2011.
  13. [13] Y. A. de Montjoye, C. A. Hidalgo, M. Verleysen, and V. D. Blondel, “Unique in the crowd: The privacy bounds of human mobility,” Scientific reports, Vol.3, 2013.
  14. [14] M. C. Gonzalez, C. A. Hidalgo, and A. L. Barabasi, “Understanding individual human mobility patterns,” Nature, Vol.453, pp. 7196, 2008.
  15. [15] A. Sevtsuk and C. Ratti, “Does urban mobility have a daily routine? Learning from the aggregate data of mobile networks,” Journal of Urban Technology, Vol.17, No.1, pp. 41-60, 2010.
  16. [16] M. G. Demissie, G. H. de Almeida Correia, and C. Bento, “Intelligent road traffic status detection system through cellular networks handover information: An exploratory study,” Transportation research part C: emerging technologies, Vol.32, pp. 76-88, 2013.
  17. [17] M. S. Iqbal, C. F. Choudhury, P. Wang, and M. C. González, “Development of origin–destination matrices using mobile phone call data,” Transportation Research Part C: Emerging Technologies, Vol.40, pp. 63-74, 2014.
  18. [18] P. Wang, T. Hunter, A. M. Bayen, K. Schechtner, and M. C. González, “Understanding road usage patterns in urban areas,” Sci. Rep., Vol.2, pp. 1001, 2012.
  19. [19] Y. Yang, D. Gerstle, P. Widhalm, D. Bauer, and M. González, “The potential of low-frequency avl data for the monitoring and control of bus performance,” Transport. Res. Rec. J. Transport. Res., 2013.
  20. [20] V. Colizza, A. Barrat, M. Barthelemy, A. J. Valleron, and A. Vespignani, “Modeling the worldwide spread of pandemic influenza: baseline case and containment interventions,” PLoS Med, Vol.4, No.1, 2007.
  21. [21] A. Pentland, “Society’s nervous system: Building effective government, energy, and public health systems,” IEEE Computer, Vol.45, pp. 31-38, 2012.
  22. [22] U. Hiroi, N. Sekiya, R. Nakajima, S. Waragai, and H. Hanahara, “Questionnaire Survey Concerning Stranded Commuters in Metropolitan Area in the Great East Japan Earthquake,” The Annals of Institute of Social Safety Science, Vol.15, pp. 343-353, 2011.
  23. [23] K. Ito, S. Aono, and N. Ohmori, “Empirical Study on Stop-Offs en Route Home in the Aftermath of an Earthquake Disaster in the Tokyo Metropolitan Area,” Journal of the City Planning Institute of Japan, Vol.48, No.3, 2013.
  24. [24] J. P. Bagrow, D. Wang, and A. L. Barabasi, “Collective response of human populations to large-scale emergencies,” PloS one, Vol.6, No.3, 2011.
  25. [25] G. R. Madey, G. Szabo, and A. L. Barabási, “WIPER: The integrated wireless phone based emergency response system,” Computational Science–ICCS 2006, pp. 417-424, Springer, 2006.
  26. [26] F. Chen, Z. Zhai, and G. Madey, “Dynamic adaptive disaster simulation: developing a predictive model of emergency behavior using cell phone and GIS data,” Proc. Workshop on Agent-Directed Simulation, 2011. Society for Computer Simulation Int.
  27. [27] T. Yabe, A. Sudo, T. Kashiyama, H. Kanasugi, and Y. Sekimoto, “Making Real-Time Predictions of People’s Irregular Movement In a Metropolitan Scale under Disaster Situations,” Proc. CUPUM, 2015.
  28. [28] Z. Fan, X. Song, R. Shibasaki, and R. Adachi, “City Momentum: An Online Approach for Crowd Behavior: Prediction at a Citywide Level,” Proc. Ubicomp, 2015.
  29. [29] X. Song, Q. Zhang, Y. Sekimoto, R. Shibasaki, N. J. Yuan, and X. Xie, “A Simulator of Human Emergency Mobility Following Disasters: Knowledge Transfer from Big Disaster Data,” AAAI, 2015.
  30. [30] E. Cho, S. A. Myers, and J. Leskovec, “Friendship and mobility: user movement in location-based social networks,” Proc. ACM SIGKDD, 2011.
  31. [31] S. Jiang, G. A. Fiore, Y. Yang, J. Ferreira Jr, E. Frazzoli, and M. C. González, “A review of urban computing for mobile phone traces: current methods, challenges and opportunities,” Proc. ACM SIGKDD, pp. 2, 2013.
  32. [32] H. W. Eves, “Elementary matrix theory,” Courier Corporation, 1980.
  33. [33] R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin, “LIBLINEAR: A library for large linear classification,” The Journal of Machine Learning Research, Vol.9, 2008.
  34. [34] S. P. Parambath, N. Usunier, and Y. Grandvalet, “Optimizing F-measures by cost-sensitive classification,” Advances in Neural Information Processing Systems, pp. 2123-2131, 2014.
  35. [35] Y. Yao and B. Zhou, “Micro and macro evaluation of classification rules,” 7th IEEE Int. Conf. on Cognitive Informatics, ICCI 2008, pp. 441-448, 2008.
  36. [36] R. Fernandez and H. Sanahuja, “Linkages between population dynamics, urbanization processes and disaster risks: a regional vision of Latin America,” 2012.
  37. [37] People Flow Project, “Center for Spatial Information Science,” University of Tokyo, http://pflow.csis.u-tokyo.ac.jp/?page_id=943 [accessed March 12, 2017]
  38. [38] United Nations Office for Disaster Risk Reduction, “The Human Cost of Weather Related Disasters,” 2015.
  39. [39] Cabinet Office of the Japanese Government, “Case Studies of Typhoons in Municipal Governments,” 2012 (in Japanese).

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Dec. 07, 2018