Abstract
Predicting individuals' next locations is a core task in human mobility modelling, with wide-ranging implications for urban planning, transportation, public policy and personalised mobility services. Traditional approaches largely depend on location embeddings learned from historical mobility patterns, limiting their ability to encode explicit spatial information, integrate rich urban semantic context, and accommodate previously unseen locations. To address these challenges, we explore the application of CaLLiPer -- a multimodal representation learning framework that fuses spatial coordinates and semantic features of points of interest through contrastive learning -- for location embedding in individual mobility prediction. CaLLiPer's embeddings are spatially explicit, semantically enriched, and inductive by design, enabling robust prediction performance even in scenarios involving emerging locations. Through extensive experiments on four public mobility datasets under both conventional and inductive settings, we demonstrate that CaLLiPer consistently outperforms strong baselines, particularly excelling in inductive scenarios. Our findings highlight the potential of multimodal, inductive location embeddings to advance the capabilities of human mobility prediction systems. We also release the code and data (this https URL) to foster reproducibility and future research.
Abstract (translated)
预测个人的下一个位置是人类移动模型中的核心任务，对城市规划、交通、公共政策和个人化移动服务有着广泛的影响。传统方法主要依赖于从历史移动模式中学习的位置嵌入，这限制了它们编码明确的空间信息的能力，整合丰富的城市语义背景，并适应之前未见过的位置。为了解决这些挑战，我们探索了CaLLiPer的应用——这是一个多模态表示学习框架，通过对比学习融合空间坐标和兴趣点的语义特征来进行位置嵌入在个人移动预测中的应用。CaLLiPer生成的嵌入是显式的、语义丰富的，并且设计上具有归纳能力，在涉及新兴地点的情况下也能实现稳健的预测性能。通过对四个公开的移动数据集进行广泛的实验，我们展示了无论是在传统的还是归纳的情境下，CaLLiPer都能持续超越强大的基准方法，尤其是在归纳场景中表现出色。我们的研究结果突显了多模态、归纳位置嵌入在提升人类移动预测系统能力方面的潜力。此外，为了促进再现性和未来的研究，我们也发布了代码和数据（此链接）。
URL
https://arxiv.org/abs/2506.14070
