Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places

TL;DR

This paper introduces CaLLiPer, a multimodal, inductive location embedding framework that combines spatial and semantic data to improve individual mobility prediction, especially for unseen locations.

Contribution

The paper presents CaLLiPer, a novel contrastive learning-based framework that produces spatially explicit, semantically rich, and inductive location embeddings for mobility prediction.

Findings

CaLLiPer outperforms baseline models in mobility prediction tasks.

It maintains high accuracy in inductive scenarios with new locations.

The approach effectively integrates spatial and semantic information.

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 (https://github.com/xlwang233/Into-the-Unknown) to foster reproducibility and future research.