Year: 2025
Type: article
Abstract: Human mobility prediction underpins diverse applications in marketing, transportation, healthcare, network management, and public safety. Accurately forecasting movement patterns requires capturing both sequential regularities—habitual commutes and recurring visits—and contextual factors—dynamic influences such as weather, events, and social interactions that can reinforce or disrupt routine. This article presents a dual-perspective survey of mobility prediction, categorizing models based on their focus on routine, context, or a fusion of both. We review key components of human mobility prediction, explore their applications across multiple domains, survey statistical and machine learning predictors, and empirically evaluate their effectiveness using large-scale mobility data under varying conditions. Based on our empirical results, we offer practical guidelines for selecting mobility prediction techniques suited to different mobility patterns. This survey provides a comprehensive foundation for researchers and practitioners aiming to develop effective human mobility prediction systems across diverse real-world scenarios.
DOI: 10.1145/3770860
Authors: Kai Zhao, Houping Xiao, Arun Rai, Jeongmin Kim
Institutions: Walmart (United States), Georgia State University
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4414915097