ISPRS Int. J. Geo-Inf. 2026, 15(7), 321; https://doi.org/10.3390/ijgi15070321 (registering DOI) - 15 Jul 2026
Abstract
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Trajectory Representations Learning (TRL) serves as a foundational technology for supporting intelligent transportation. However, models trained on real-world data often suffer from performance degradation caused by inherent spatiotemporal distribution bias, which reflects the heterogeneity of urban structures and human movement behaviors. This leads
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Trajectory Representations Learning (TRL) serves as a foundational technology for supporting intelligent transportation. However, models trained on real-world data often suffer from performance degradation caused by inherent spatiotemporal distribution bias, which reflects the heterogeneity of urban structures and human movement behaviors. This leads to representations that overfit to frequent patterns, resulting in weak robustness and limited generalization to sparse or atypical trajectories. To address these issues, this paper presents a novel perspective, anchor-guided balanced learning, and instantiates it with a framework, AnchorTRL. AnchorTRL introduces anchors to proactively construct a balanced semantic space instead of passively fitting the empirical data distribution. Specifically, AnchorTRL designs a spatiotemporal anchor identification algorithm to recognize trajectory anchors that comprehensively cover the data manifold. And, it proposes a calculation method to measure all trajectories’ semantic similarity with anchors. Additionally, it develops an anchor-based balanced sampling strategy to mitigate the dominance of frequent patterns and steer the model towards learning a more balanced representation. Finally, it constructs a multi-task contrastive learning objective with adaptive constraints to enhance the aggregation of semantically similar trajectories. Experimental results show that AnchorTRL outperforms existing baseline methods in tasks such as travel time estimation and similar trajectory queries, demonstrating its effectiveness and robustness. This research provides methodological support for constructing more reliable trajectory representation learning models, and offers new insights for optimizing intelligent transportation applications under spatiotemporal biases.
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