App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 8

Social-awareness driven spatiotemporal correlation deep decoupling for joint prediction of trajectory and arrival time

He Xingyua,b
Hu Mingminga
Yang Guisongb
a. College of Publishing, b. School of Optical-Electrical & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2025.12.0509

Abstract

In user trajectory prediction within urban scenarios, existing methods lack an inference mechanism from social semantics to dynamic physical constraints, which limits the deep decoupling of spatiotemporal correlations. This paper proposes a joint user trajectory and arrival time prediction method based on the social-aware driven deep decoupling of spatiotemporal correlations, adopting an architecture characterized by "Individual-Social Dual-Layer Encoding + Spatiotemporal Correlation Constrained Decoding. " First, the method employs a multi-view feature extraction module to construct fundamental priors of user spatiotemporal behaviors. Then, it introduces a context-aware heterogeneous social graph Transformer to explicitly model the modulation effects of different social relationship types on individual moving speeds and travel rhythms under specific spatiotemporal contexts, thereby generating velocity distribution representations infused with social dynamics. Finally, a physics-constrained decoder integrates the derived velocity features into the "distance-velocity-time" physical manifold to ensure the consistency of prediction results. Experiments on large-scale real-world datasets demonstrate that the proposed method improves location prediction accuracy by 10.18%, reduces Estimated Time of Arrival (ETA) estimation error by 12.3%, and enhances physical consistency by 84%, verifying the effectiveness of the unified modeling of individual, social, and physical constraints for trajectory prediction.

Foundation Support

国家自然科学基金资助项目(61602305,61802257)
上海市自然科学基金资助项目(18ZR1426000,19ZR1477600)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.12.0509
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 8

Publish History

[2026-04-22] Accepted Paper

Cite This Article

何杏宇, 胡洺铭, 杨桂松. 基于社交感知驱动深度时空解耦的轨迹-到达时间联合预测 [J]. 计算机应用研究, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0509. (He Xingyu, Hu Mingming, Yang Guisong. Social-awareness driven spatiotemporal correlation deep decoupling for joint prediction of trajectory and arrival time [J]. Application Research of Computers, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0509. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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