LE4Mob: Towards Inductive, Distance-Aware and General-Purpose Location Embedding for Human Mobility Modelling

LE4Mob：面向人类移动性建模的归纳式、距离感知及通用位置嵌入

Abstract: 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.

摘要： 位置表征为移动性模型提供了关于空间位置、功能特征以及地点间关系的基础信息。然而，现有的嵌入方法往往依赖于移动性观测数据，无法表示未见过的地点，且在保持地理距离方面的约束较弱。这限制了它们在不同数据集和移动性任务间的复用。

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.

为了解决这些局限性，我们提出了 LE4Mob，这是一个用于移动性建模的归纳式、距离感知且源于地理信息的位置嵌入框架。LE4Mob 扩展了对比语言-位置预训练方法，并引入了一个距离感知正则化目标，旨在促使嵌入空间能够保留空间关系。

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.

通过地理上下文进行预训练，LE4Mob 能够编码丰富的空间语义信息，并以归纳方式为未见过的地点生成嵌入。它对下游移动性任务监督的独立性，也使其能够在不同的移动性任务之间进行迁移。

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.

我们在个体层面的下一地点预测和群体层面的通勤流生成任务上对 LE4Mob 进行了评估。在多个数据集和研究区域进行的实验表明，LE4Mob 的表现优于强基准模型，特别是在归纳式场景以及下游模型直接依赖位置嵌入间交互的情况下，具有显著优势。这些发现证明了距离感知、源于地理的位置表征作为人类移动性建模可复用基础的潜力。