What question did this study set out to answer?

To develop a trajectory recovery framework that reconstructs missing location points by jointly modeling global population-wide spatial transitions and personalized multi-dimensional semantics.

August 15, 2026ISPRS International Journal of Geo-InformationOpen Access

Trajectory Recovery via Global Spatial Dependencies and Local Multi-Factor Semantics

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Authors

CJCheng JinTsinghua University DXDaozhu Xu XZXinlei ZhangQiqihar University

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Implication

Computational modeling demonstrates improved trajectory recovery in missing mobility datasets, highlighting the benefit of joint global flow and local semantic modeling.

Key Points

To develop a trajectory recovery framework that reconstructs missing location points by jointly modeling global population-wide spatial transitions and personalized multi-dimensional semantics.
Designed GLTrajRec, integrating global trajectory flow graphs for population-level mobility constraints with multi-factor local semantic embeddings for individual user behaviors.
Utilized a Transformer-based bidirectional encoder–decoder architecture coupled with a graph-based transition matrix to decode missing trajectory sequences.
Evaluated performance on two real-world mobility datasets against traditional trajectory recovery baseline methods.
Achieved mean average precision (MAP) scores of 0.7181 on the first dataset and 0.7345 on the second dataset.
Outperformed traditional trajectory recovery methods with an accuracy improvement of approximately 5% to 7%.

Cite This Study

Jin et al. (2026) studied this question.

synapsesocial.com/papers/6a8019fe75c2e31742c86546 https://doi.org/10.3390/ijgi15080364

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Implication

Computational modeling demonstrates improved trajectory recovery in missing mobility datasets, highlighting the benefit of joint global flow and local semantic modeling.

Trajectory Recovery via Global Spatial Dependencies and Local Multi-Factor Semantics

Discussion

Member takes

Implication

Key Points

Cite This Study

Trajectory Recovery via Global Spatial Dependencies and Local Multi-Factor Semantics

Discussion

Member takes

Implication

Key Points

Cite This Study

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