  Modeling and predicting human mobility trajectories in urban areas is an
essential task for various applications. The recent availability of large-scale
human movement data collected from mobile devices have 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 fair performance comparisons between methods. To
this end, we created an open-source, anonymized, metropolitan scale, and
longitudinal (90 days) dataset of 100,000 individuals' human mobility
trajectories, using mobile phone location data. 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 promote the use of the
dataset, we will host a human mobility prediction data challenge (`HuMob
Challenge 2023') using the human mobility dataset, which will be held in
conjunction with ACM SIGSPATIAL 2023.
