GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values

• URL: http://arxiv.org/abs/2508.14083v1
• Date: Wed, 13 Aug 2025 03:30:45 GMT
• Title: GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values
• Authors: Songyu Ke, Chenyu Wu, Yuxuan Liang, Xiuwen Yi, Yanping Sun, Junbo Zhang, Yu Zheng,
• Abstract summary: Crowd flow at Points of Interest (POIs) is pivotal for effective traffic management, public service acquisition and urban planning.<n>Despite this, due to the limitations of urban sensing techniques, the data quality from most sources is inadequate for monitoring crowd flow at each POI.<n>This renders the inference of accurate crowd flow from low-quality data a critical and challenging task.
• Score: 26.941201983071057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate acquisition of crowd flow at Points of Interest (POIs) is pivotal for effective traffic management, public service, and urban planning. Despite this importance, due to the limitations of urban sensing techniques, the data quality from most sources is inadequate for monitoring crowd flow at each POI. This renders the inference of accurate crowd flow from low-quality data a critical and challenging task. The complexity is heightened by three key factors: 1) \emph{The scarcity and rarity of labeled data}, 2) \emph{The intricate spatio-temporal dependencies among POIs}, and 3) \emph{The myriad correlations between precise crowd flow and GPS reports}. To address these challenges, we recast the crowd flow inference problem as a self-supervised attributed graph representation learning task and introduce a novel \underline{C}ontrastive \underline{S}elf-learning framework for \underline{S}patio-\underline{T}emporal data (\model). Our approach initiates with the construction of a spatial adjacency graph founded on the POIs and their respective distances. We then employ a contrastive learning technique to exploit large volumes of unlabeled spatio-temporal data. We adopt a swapped prediction approach to anticipate the representation of the target subgraph from similar instances. Following the pre-training phase, the model is fine-tuned with accurate crowd flow data. Our experiments, conducted on two real-world datasets, demonstrate that the \model pre-trained on extensive noisy data consistently outperforms models trained from scratch.

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