Zhang, Y., Yu, Z., Dang, M., Xu, E., Guo, B., Liang, Y., Yin, Y., Zimmermann, R. (2024). Predictability in Human Mobility: From Individual to Collective (Vision Paper). ACM Transactions on Spatial Algorithms and Systems, 10(2), 1–17. https://doi.org/10.1145/3656640
Abstract:
Human mobility is the foundation of urban dynamics and its prediction significantly benefits various downstream location-based services. Nowadays, while deep learning approaches are dominating the mobility prediction field where various model architectures/designs are continuously updating to push up the prediction accuracy, there naturally arises a question: whether these models are sufficiently good to reach the best possible prediction accuracy? To answer this question, predictability study is a method that quantifies the inherent regularities of the human mobility data and links the result to that limit. Mainstream predictability studies achieve this by analyzing the individual trajectories and merging all individual results to obtain an upper bound. However, the multiple individuals composing the city are not totally independent and the individual behavior is heavily influenced by its implicit or explicit surroundings. Therefore, the collective factor should be considered in the mobility predictability measurement, which has not been addressed before. This vision paper points out this concern and envisions a few potential research problems along such an individual-to-collective transition from both data and methodology aspects. We hope the discussion in this paper sheds some light on the human mobility predictability community.
License type:
Publisher Copyright
Funding Info:
This work was supported in part by the National Natural Science Foundation of China.