Mobile Analytics Techniques: Survey, Evaluation, and Guidelines

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.