Individual trajectories, rich in human-environment interaction information across space and time, serve as vital inputs for geospatial foundation models (GeoFMs). However, existing attempts at learning trajectory representations have overlooked the implicit spatial-temporal dependency within trajectories, failing to encode such dependency in a deep learning-friendly format. That poses a challenge in obtaining general-purpose trajectory representations. Therefore, this paper proposes a spatial-temporal joint representation learning method (ST-GraphRL) to formalize learnable spatial-temporal dependencies into trajectory representations. The proposed ST-GraphRL consists of three compositions: (i) a weighted directed spatial-temporal graph to explicitly construct mobility interactions in both space and time dimensions; (ii) a two-stage jointly encoder (i.e., decoupling and fusion), to learn entangled spatial-temporal dependencies by independently decomposing and jointly aggregating space and time information; (iii) a decoder guides ST-GraphRL to learn explicit mobility regularities by simulating the spatial-temporal distributions of trajectories. Tested on three real-world human mobility datasets, the proposed ST-GraphRL outperformed all the baseline models in predicting movement spatial-temporal distributions and preserving trajectory similarity with high spatial-temporal correlations. Analyzing spatial-temporal features presented in latent space validates that ST-GraphRL understands spatial-temporal patterns. This study may also benefit representation learnings of other geospatial data to achieve general-purpose data representations and advance GeoFMs development.

翻译：个体轨迹蕴含丰富的空间-时间维度人境交互信息，是地理空间基础模型（GeoFMs）的关键输入。然而，现有轨迹表征学习方法忽视了轨迹中隐含的时空依赖性，未能以深度学习友好格式编码这种依赖关系，导致难以获取通用型轨迹表征。为此，本文提出时空联合表征学习方法（ST-GraphRL），将可学习的时空依赖性形式化为轨迹表征。该方法包含三个组件：（i）构建加权有向时空图，显式建模空间与时间维度上的移动交互；（ii）设计两阶段联合编码器（即解耦与融合），通过独立分解和联合聚合空间与时间信息，学习纠缠的时空依赖性；（iii）解码器通过模拟轨迹的时空分布，引导ST-GraphRL学习显式的移动规律。在三个真实人类移动数据集上的实验表明，所提ST-GraphRL在预测移动时空分布及保持高时空相关性的轨迹相似度方面均优于所有基线模型。对隐空间呈现的时空特征进行分析，验证了ST-GraphRL能够理解时空模式。本研究亦可惠及其他地理空间数据的表征学习，为构建通用型数据表征及推动GeoFMs发展提供支持。