research-article

Context-aware Deep Model for Joint Mobility and Time Prediction

Authors:

Chenliang LiAuthors Info & Claims

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

Pages 106 - 114

https://doi.org/10.1145/3336191.3371837

Published: 22 January 2020 Publication History

Abstract

Mobility prediction, which is to predict where a user will arrive based on the user's historical mobility records, has attracted much attention. We argue that it is more useful to know not only where but also when a user will arrive next in many scenarios such as targeted advertising and taxi service. In this paper, we propose a novel context-aware deep model called DeepJMT for jointly performing mobility prediction (to know where) and time prediction (to know when). The DeepJMT model consists of (1) a hierarchical recurrent neural network (RNN) based sequential dependency encoder, which is more capable of capturing a user's mobility regularities and temporal patterns compared to vanilla RNN based models; (2) a spatial context extractor and a periodicity context extractor to extract location semantics and the user's periodicity, respectively; and (3) a co-attention based social & temporal context extractor which could extract the mobility and temporal evidence from social relationships. Experiments conducted on three real-world datasets show that DeepJMT outperforms the state-of-the-art mobility prediction and time prediction methods.

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Cited By

Yao YJiang YYu QYuan JLi JXu JLiu SZhang H(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
https://doi.org/10.3390/ijgi13070261
Duan MQian YZhao LZhou ZRasheed ZYu RShafique K(2024)Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural FrameworkProceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection10.1145/3681765.3698454(56-67)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681765.3698454
Hsu STung EKrumm JShahabi CShafique K(2024)TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal ModelProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691303(362-371)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691303
Show More Cited By

Index Terms

Context-aware Deep Model for Joint Mobility and Time Prediction
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing design and evaluation methods
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems
      1. Location based services

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cover image ACM Conferences

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

January 2020

950 pages

ISBN:9781450368223

DOI:10.1145/3336191

General Chairs:
James Caverlee
Texas A&M University
,
Xia "Ben" Hu
Texas A&M University
,
Program Chairs:
Mounia Lalmas
Spotify
,
Wei Wang
University of California, Los Angeles

Copyright © 2020 ACM.

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Published: 22 January 2020

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February 3 - 7, 2020

TX, Houston, USA

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Cited By

Yao YJiang YYu QYuan JLi JXu JLiu SZhang H(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
https://doi.org/10.3390/ijgi13070261
Duan MQian YZhao LZhou ZRasheed ZYu RShafique K(2024)Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural FrameworkProceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection10.1145/3681765.3698454(56-67)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681765.3698454
Hsu STung EKrumm JShahabi CShafique K(2024)TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal ModelProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691303(362-371)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691303
Yu QWang HLiu YJin DLi YZhu LFeng J(2024)Mobility Prediction via Rule-enhanced Knowledge GraphACM Transactions on Knowledge Discovery from Data10.1145/367701918:9(1-21)Online publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1145/3677019
Jiang FWang HLi Y(2024)VesNet: A Vessel Network for Jointly Learning Route Pattern and Future TrajectoryACM Transactions on Intelligent Systems and Technology10.1145/363937015:2(1-25)Online publication date: 18-Jan-2024
https://dl.acm.org/doi/10.1145/3639370
Sun TFu KHuang WZhao KGong YChen MBaeza-Yates RBonchi F(2024)Going Where, by Whom, and at What Time: Next Location Prediction Considering User Preference and Temporal RegularityProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671916(2784-2793)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671916
Balsebre PHuang WCong GLi YSerra ESpezzano F(2024)City Foundation Models for Learning General Purpose Representations from OpenStreetMapProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679662(87-97)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679662
Wang CLiang YTan GAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)CityCAN: Causal Attention Network for Citywide Spatio-Temporal ForecastingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635764(702-711)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635764
Huang ZXu SWang MWu HXu YJin Y(2024)Human mobility prediction with causal and spatial-constrained multi-task networkEPJ Data Science10.1140/epjds/s13688-024-00460-713:1Online publication date: 19-Mar-2024
https://doi.org/10.1140/epjds/s13688-024-00460-7
Gao QHong JXu XKuang PZhou FTrajcevski G(2024)Predicting Human Mobility Via Self-Supervised Disentanglement LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3317175(1-16)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3317175
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