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Robust Location Prediction over Sparse Spatiotemporal Trajectory Data: Flashback to the Right Moment!

Published: 30 September 2023 Publication History

Abstract

As a fundamental problem in human mobility modeling, location prediction forecasts a user’s next location based on historical user mobility trajectories. Recurrent neural networks (RNNs) have been widely used to capture sequential patterns of user visited locations for solving location prediction problems. Due to the sparse nature of real-world user mobility trajectories, existing techniques strive to improve RNNs by incorporating spatiotemporal contexts into the recurrent hidden state passing process of RNNs using context-parameterized transition matrices or gates. However, such a scheme mismatches universal spatiotemporal mobility laws and thus cannot fully benefit from rich spatiotemporal contexts encoded in user mobility trajectories. Against this background, we propose Flashback++, a general RNN architecture designed for modeling sparse user mobility trajectories. It not only leverages rich spatiotemporal contexts to search past hidden states with high predictive power but also learns to optimally combine them via a hidden state re-weighting mechanism, which significantly improves the robustness of the models against different settings and datasets. Our extensive evaluation compares Flashback++ against a sizable collection of state-of-the-art techniques on two real-world location-based social networks datasets and one on-campus mobility dataset. Results show that Flashback++ not only consistently and significantly outperforms all baseline techniques by 20.56% to 44.36% but also achieves better robustness of location prediction performance against different model settings (different RNN architectures and numbers of hidden states to flash back), different levels of trajectory sparsity, and different train-testing splitting ratios than baselines, yielding an improvement of 31.05% to 94.60%.

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  • (2024)A GIS-Based Framework for Synthesizing City-Scale Long-Term Individual-Level Spatial–Temporal MobilityISPRS International Journal of Geo-Information10.3390/ijgi1307026113:7(261)Online publication date: 22-Jul-2024
  • (2024)A Novel Multimodal Long-Term Trajectory Prediction Scheme for Heterogeneous User Behavior PatternsIEEE Transactions on Mobile Computing10.1109/TMC.2024.342786223:12(13275-13291)Online publication date: 1-Dec-2024
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cover image ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology  Volume 14, Issue 5
October 2023
472 pages
ISSN:2157-6904
EISSN:2157-6912
DOI:10.1145/3615589
  • Editor:
  • Huan Liu
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 September 2023
Online AM: 18 August 2023
Accepted: 08 August 2023
Revised: 24 July 2023
Received: 02 March 2023
Published in TIST Volume 14, Issue 5

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Author Tags

  1. Location prediction
  2. sparse trajectory
  3. user mobility
  4. recurrent neural networks

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  • Research-article

Funding Sources

  • University of Macau
  • Science and Technology Development Fund, Macau SAR
  • UIC research grant
  • European Research Council
  • SKL-IOTSC, University of Macau

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View all
  • (2024)A GIS-Based Framework for Synthesizing City-Scale Long-Term Individual-Level Spatial–Temporal MobilityISPRS International Journal of Geo-Information10.3390/ijgi1307026113:7(261)Online publication date: 22-Jul-2024
  • (2024)A Novel Multimodal Long-Term Trajectory Prediction Scheme for Heterogeneous User Behavior PatternsIEEE Transactions on Mobile Computing10.1109/TMC.2024.342786223:12(13275-13291)Online publication date: 1-Dec-2024
  • (2024)Multi-task Learning of Heterogeneous Hypergraph Representations in LBSNsAdvanced Data Mining and Applications10.1007/978-981-96-0821-8_11(161-177)Online publication date: 3-Dec-2024

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