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Modeling and predicting human mobility trajectories in urban areas is anessential task for various applications. The recent availability of large-scalehuman movement data collected from mobile devices have enabled the developmentof complex human mobility prediction models. However, human mobility predictionmethods are often trained and tested on different datasets, due to the lack ofopen-source large-scale human mobility datasets amid privacy concerns, posing achallenge towards conducting fair performance comparisons between methods. Tothis end, we created an open-source, anonymized, metropolitan scale, andlongitudinal (90 days) dataset of 100,000 individuals' human mobilitytrajectories, using mobile phone location data. The location pings arespatially and temporally discretized, and the metropolitan area is undisclosedto protect users' privacy. The 90-day period is composed of 75 days ofbusiness-as-usual and 15 days during an emergency. To promote the use of thedataset, we will host a human mobility prediction data challenge (`HuMobChallenge 2023') using the human mobility dataset, which will be held inconjunction with ACM SIGSPATIAL 2023.

Metropolitan Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectories