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

Paper:

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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
Creative Commons License
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.
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