論 文Papers

CONFERENCE (INTERNATIONAL)

Predicting irregular individual movement following frequent mid-level disasters using location data from smartphones

Takahiro Yabe(The University of Tokyo), Kota Tsubouchi, Akihito Sudo(The University of Tokyo), Yoshihide Sekimoto(The University of Tokyo)

GIS '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2016/10

Category:

機械学習 (Machine Learning) データサイエンス (Data Science) その他の取り組み (Misc.)

Abstract:
Mid-level 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 irregular movement of individuals following such disasters is crucial for managing urban systems. Past survey results show that mid-level disasters do not force many individuals to evacuate away from their homes, but do cause irregular movement by significantly delaying the movement timings, resulting in severe congestion in urban transportation. We propose a novel method that predicts such irregularity of individuals' movements in several mid-level disasters using various types of features including the victims' usual movement patterns, disaster information, and geospatial information of victims' locations. Using real GPS data of 1 million people in Tokyo, we show that our method can predict mobility delay with high accuracy,
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