Smart City Gnosys
Smart city article details
| Title | Modelling Super-Diffusion In Urban Human Mobility: A Quantum Walk Approach |
|---|---|
| ID_Doc | 37705 |
| Authors | Tan L.; Yuan L.; Liu Z.; Zhong T.; Ye X.; Yu Z. |
| Year | 2025 |
| Published | Cities, 163 |
| DOI | http://dx.doi.org/10.1016/j.cities.2025.106000 |
| Abstract | Super-diffusion in urban human mobility refers to the faster-than-linear increase in the mean squared displacement (MSD) of the mobility collective over time. Accurate modeling of super-diffusion in urban human mobility is a key focus in various research fields and industries. However, there are few urban human mobility models that can capture super-diffusion. To address this gap, this paper proposes a quantum walk-based urban human mobility model (UHM-QW). In quantum walks, the spread of a walker exhibits super-diffusion properties. UHM-QW generates mobility patterns with varying diffusion rates by quantum walks and models the collective super-diffusion as a weighted combination of important mobility patterns. Experiments show that UHM-QW can accurately reproduce the super-diffusion process and its temporal fluctuation characteristics. Compared to the reference models (Random Walk, SeqGAN, LSTM-TrajGAN), UHM-QW achieves greater fit accuracy (R2), better retains temporal structure (NDTW-t), and captures fluctuation details (NDTW-r). Analysis of important mobility patterns reveals that faster-diffusing collectives are driven by faster diffusion-rate patterns, while those with greater variability arise from patterns with substantial differences in diffusion-rates. Additionally, a single dominant mobility pattern effectively reproduces the super-diffusion process, reducing the model's complexity and enhancing its interpretability. In conclusion, this paper reveals the correspondence between quantum walk dynamics and human mobility patterns, providing a new perspective for mining human mobility laws in cities. © 2025 Elsevier Ltd |
| Author Keywords | Diffusion process; Human mobility; Quantum walk; Smart city; Super-diffusion |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
13826  | 0.865 | Medina P.; Carrasco S.C.; Jofré M.S.; Rogan J.; Valdivia J.A. | Characterizing Diffusion Processes In City Traffic | Chaos, Solitons and Fractals, 165 (2022) |
| 31215 | 0.859 | Mizzi C.; Baroncini A.; Fabbri A.; Micheli D.; Vannelli A.; Criminisi C.; Jean S.; Bazzani A. | Individual Mobility Deep Insight Using Mobile Phones Data | EPJ Data Science, 12, 1 (2023) |
| 5157 | 0.858 | Solmaz G.; Turgut D. | A Survey Of Human Mobility Models | IEEE Access, 7 (2019) |
| 25800 | 0.852 | Liu S.; Xu M.; Long Y. | Exploring The Spatiotemporal Pattern Of Urban Human Flows From The Perspective Of Dynamic Network | 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2021 (2021) |
| 40085 | 0.851 | Fan Z.; Yang X.; Yuan W.; Jiang R.; Chen Q.; Song X.; Shibasaki R. | Online Trajectory Prediction For Metropolitan Scale Mobility Digital Twin | GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems (2022) |
| 16641 | 0.85 | Zhang Y.; Shi W.; Tan S.; Mou J.; Zhou Y.; Yu H.; Lu X. | Cross-Scale Comparison Methods For Urban Mobility Models; [城市移动流量模型的跨尺度比较方法] | Wuli Xuebao/Acta Physica Sinica, 74, 14 (2025) |