Individual human location trajectory and check-in data have been the driving force for human mobility research in recent years. How- ever, existing human mobility datasets are very limited in size and representativeness. For example, one of the largest and most commonly used datasets of individual human location trajectories, GeoLife, captures fewer than two hundred individuals. To help fill this gap, this Data and Resources paper leverages an existing data generator based on fine-grained simulation of individual hu- man patterns of life to produce large-scale trajectory, check-in, and social network data. In this simulation, individual human agents commute between their home and work locations, visit restaurants to eat, and visit recreational sites to meet friends. We provide large datasets of months of simulated trajectories for two example regions in the United States: San Francisco and New Orleans. In addition to making the datasets available, we also provide instructions on how the simulation can be used to re-generate data, thus allow- ing researchers to generate the data locally without downloading prohibitively large files.
Hossein Amiri, Shiyang Ruan, Joon-Seok Kim, Hyunjee Jin, Hamdi Kavak, Andrew Crooks, Dieter Pfoser, Carola Wenk, Andreas Zufle, and Boleslaw Szymanski
@inproceedings{amiri2023massive,
author = {Amiri, Hossein and Ruan, Shiyang and Kim, Joon-Seok and Jin, Hyunjee and Kavak, Hamdi and Crooks, Andrew and Pfoser, Dieter and Wenk, Carola and Zufle, Andreas},
title = {Massive Trajectory Data Based on Patterns of Life},
year = {2023},
isbn = {9798400701689},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589132.3625592},
doi = {10.1145/3589132.3625592},
abstract = {Individual human location trajectory and check-in data have been the driving force for human mobility research in recent years. However, existing human mobility datasets are very limited in size and representativeness. For example, one of the largest and most commonly used datasets of individual human location trajectories, GeoLife, captures fewer than two hundred individuals. To help fill this gap, this Data and Resources paper leverages an existing data generator based on fine-grained simulation of individual human patterns of life to produce large-scale trajectory, check-in, and social network data. In this simulation, individual human agents commute between their home and work locations, visit restaurants to eat, and visit recreational sites to meet friends. We provide large datasets of months of simulated trajectories for two example regions in the United States: San Francisco and New Orleans. In addition to making the datasets available, we also provide instructions on how the simulation can be used to re-generate data, thus allowing researchers to generate the data locally without downloading prohibitively large files.},
booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},
articleno = {49},
numpages = {4},
location = {, Hamburg, Germany, },
series = {SIGSPATIAL '23}
}