Publications
JOURNAL (INTERNATIONAL) YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories
Takahiro Yabe (New York University), Kota Tsubouchi, Toru Shimizu, Yoshihide Sekimoto (The University of Tokyo), Kaoru Sezaki (The University of Tokyo), Esteban Moro (Massachusetts Institute of Technology), Alex Pentland (Massachusetts Institute of Technology)
Scientific Data (Sci. Data)
April 23, 2024
Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications including transportation modeling, disaster management, and urban planning. The recent availability of large-scale human movement data collected from mobile devices has enabled the development of complex human mobility prediction models. However, human mobility prediction methods are often trained and tested on different datasets, due to the lack of open-source large-scale human mobility datasets amid privacy concerns, posing a challenge towards conducting transparent performance comparisons between methods. To this end, we created an open-source, anonymized, metropolitan scale, and longitudinal (75 days) dataset of 100,000 individuals' human mobility trajectories, using mobile phone location data provided by Yahoo Japan Corporation (currently renamed to LY Corporation), named YJMob100K. The location pings are spatially and temporally discretized, and the metropolitan area is undisclosed to protect users' privacy. The 90-day period is composed of 75 days of business-as-usual and 15 days during an emergency, to test human mobility predictability during both normal and anomalous situations.
Paper :
YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories
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