Mobility Prediction Using Markov Models: A Survey

Abstract

This comprehensive review critically examines the pressing need for accurate human mobility prediction, which is essential for urban planning, traffic engineering, and transportation systems. The study focuses on the data modeling capabilities and predictive accuracy of Markov-based model algorithms, with a special emphasis on Hidden Markov Models (HMMs). It explores their applications across a variety of data types, ranging from sporadic social media check-ins to continuous GPS tracking. By synthesizing research studies from the past decade, this paper assesses the effectiveness of these methodologies in handling diverse data forms. Furthermore, it addresses significant challenges such as data sparsity, accuracy, and computational efficiency, and discusses future research directions that could potentially enhance the accuracy of mobility predictions.

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