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
**Yahoo Japan Corporation, Tokyo, Japan
-  United Nations System Task Team on the POST-2015 UN Development Agenda, “Disaster Risk and Resilience,” 2012.
-  Japanese Government Cabinet Office, “Final Report of Council for Stranded Commuters in Tokyo Metropolitan Earthquake,” 2012(in Japanese).
-  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.
-  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.
-  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.
-  Q. Wang and J. E. Taylor, “Quantifying human mobility perturbation and resilience in Hurricane Sandy,” PLoS one, Vol.9, No.11, 2014.
-  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.
-  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.
-  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.
-  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.
-  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.
-  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.
-  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.
-  M. C. Gonzalez, C. A. Hidalgo, and A. L. Barabasi, “Understanding individual human mobility patterns,” Nature, Vol.453, pp. 7196, 2008.
-  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.
-  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.
-  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.
-  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.
-  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.
-  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.
-  A. Pentland, “Society’s nervous system: Building effective government, energy, and public health systems,” IEEE Computer, Vol.45, pp. 31-38, 2012.
-  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.
-  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.
-  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.
-  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.
-  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.
-  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.
-  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.
-  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.
-  E. Cho, S. A. Myers, and J. Leskovec, “Friendship and mobility: user movement in location-based social networks,” Proc. ACM SIGKDD, 2011.
-  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.
-  H. W. Eves, “Elementary matrix theory,” Courier Corporation, 1980.
-  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.
-  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.
-  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.
-  R. Fernandez and H. Sanahuja, “Linkages between population dynamics, urbanization processes and disaster risks: a regional vision of Latin America,” 2012.
-  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]
-  United Nations Office for Disaster Risk Reduction, “The Human Cost of Weather Related Disasters,” 2015.
-  Cabinet Office of the Japanese Government, “Case Studies of Typhoons in Municipal Governments,” 2012 (in Japanese).
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.