The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
DeepMove: Predicting Human Mobility with Attentional Recurrent Networks
Open accessAbstract
Human mobility prediction is of great importance for a wide spectrum of location-based applications. However, predicting mobility is not trivial because of three challenges: 1) the complex sequential transition regularities exhibited with time-dependent and high-order nature; 2) the multi-level periodicity of human mobility; and 3) the heterogeneity and sparsity of the collected trajectory data. In this paper, we propose DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories. In DeepMove, we first design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern the human mobility. Then, we propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way, which effectively utilizes the periodicity nature to augment the recurrent neural network for mobility prediction. We perform experiments on three representative real-life mobility datasets, and extensive evaluation results demonstrate that our model outperforms the state-of-the-art models by more than 10%. Moreover, compared with the state-of-the-art neural network models, DeepMove provides intuitive explanations into the prediction and sheds light on interpretable mobility prediction.
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Self-citationDynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting
Han, L., Du, B., Sun, L., Fu, Y., Lv, Y., & Xiong, H. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467275 · Cited by 281
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Trackintel: An open-source Python library for human mobility analysis
Martin, H., Hong, Y., Wiedemann, N., Bucher, D., & Raubal, M. (2023) · Computers, Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2023.101938 · Cited by 58
Healthwalks: sensing fine-grained individual health condition via mobility data
Lin, Z., Lyu, S., Cao, H., Xu, F., Wei, Y., Samet, H., & Li, Y. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3432229 · Cited by 18
Self-citation
Metadata
- Authors
- Feng, J., Li, Y., Zhang, C., Sun, F., Meng, F., Guo, A., & Jin, D.
- Venue
- Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
- Publisher
- ACM Press
- Published
- 2018
- Pages
- 1459-1468
- OpenAlex
- W2788114581
- Cited by
- 707
- References
- 38
- FWCI
- 132.95
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Feng, J., Li, Y., Zhang, C., Sun, F., Meng, F., Guo, A., & Jin, D. (2018). DeepMove: Predicting Human Mobility with Attentional Recurrent Networks. Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18. https://doi.org/10.1145/3178876.3186058
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