LE4Mob：面向人类移动建模的归纳式、距离感知与通用位置嵌入
LE4Mob: Towards Inductive, Distance-Aware and General-Purpose Location Embedding for Human Mobility Modelling
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中文总结 AI 辅助
LE4Mob提出一种归纳式、距离感知且源自地理信息的位置嵌入框架，通过对比预训练和距离正则化，在下一位置预测和通勤流生成任务中优于强基线，支持未见位置并跨任务迁移。
中文摘要 AI 辅助
位置表示为移动模型提供了关于地点的空间位置、功能特征和相互关系的基本信息。然而，现有的嵌入方法通常依赖于移动观测数据，无法表示未见过的位置，并且缺乏对保留地理距离的强约束。这限制了它们在不同数据集和移动任务中的复用。为解决这些局限性，我们提出了LE4Mob，一个用于移动建模的归纳式、距离感知且源自地理信息的位置嵌入框架。LE4Mob扩展了对比式语言-位置预训练，同时引入了一个距离感知的正则化目标，鼓励嵌入空间保留空间关系。通过从地理上下文进行预训练，LE4Mob能够编码丰富的空间语义信息，并归纳性地为未见过的位置生成嵌入。它不依赖于下游移动任务的监督，因此可迁移到不同的移动任务中。我们在个体级别的下一位置预测和群体级别的通勤流生成任务上评估了LE4Mob。跨多个数据集和研究区域的实验表明，LE4Mob优于强基线模型，在归纳设置以及下游模型直接依赖位置嵌入之间交互的场景中尤为突出。这些发现展示了距离感知、源自地理信息的位置表示作为人类移动建模可复用基础的潜力。
英文摘要
Location representations provide mobility models with fundamental information about the spatial position, functional characteristics, and relationships of places. However, existing embeddings are often dependent on mobility observations, unable to represent unseen locations, and weakly constrained to retain geographic distance. This limits their reuse across datasets and mobility tasks. To address these limitations, we propose LE4Mob, an inductive, distance-aware, and geography-derived location embedding framework for mobility modelling. LE4Mob extends contrastive language-location pre-training while introducing a distance-aware regularisation objective that encourages the embedding space to preserve spatial relationships. Pre-trained from geographic context, LE4Mob can encode rich spatial-semantic information and generate embeddings for unseen locations inductively. Its independence from downstream mobility task supervision also makes it transferable across different mobility tasks. We evaluate LE4Mob on individual-level next location prediction and population-level commuter flow generation. Experiments across multiple datasets and study areas show that LE4Mob outperforms strong baselines, with particular advantages in inductive settings and when downstream models rely directly on interactions between location embeddings. These findings demonstrate the potential of distance-aware, geography-derived location representations as reusable foundations for human mobility modelling.
发表机构
- University College London（伦敦大学学院）
- The Chinese University of Hong Kong（香港中文大学）
- Wuhan University（武汉大学）
- The Alan Turing Institute（艾伦·图灵研究所）
机构由 AI 辅助整理，请以论文原文为准。
