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Abstract
This paper presents an in-depth bibliometric analysis of the evolving landscape of human trajectory prediction through the lens of machine learning (ML), leveraging both bibliographic coupling and keyword co-occurrence methodologies. Drawing from a curated corpus of high-impact publications indexed in Scopus from 2015 to 2025, the study unveils the intellectual and conceptual structure of the field. The bibliographic coupling analysis identifies five dominant thematic clusters: (1) sensor-driven human activity and mobility monitoring, (2) deep learning approaches for pedestrian and crowd trajectory forecasting, (3) autonomous vehicles and intelligent driving behaviors, (4) multi-agent coordination and vehicle-to-vehicle dynamics, and (5) vision-based trajectory estimation and behavioral understanding. In parallel, the co-occurrence analysis reveals four emergent conceptual clusters centered around: (1) autonomous systems and environmental perception, (2) human-centered motion modeling, (3) predictive artificial intelligence (AI) and spatiotemporal learning, and (4) cognitive modeling and adaptive learning architectures. The findings underscore a paradigm shift toward hybrid deep learning frameworks, socially-aware multi-agent models, and explainable trajectory forecasting systems. Furthermore, the study identifies key future directions including graph-based motion prediction, privacy-preserving analytics, and real-time edge-AI deployments. This bibliometric roadmap serves as a foundational reference for scholars, system architects, and policymakers engaged in advancing intelligent mobility systems and human-machine interaction in complex environments. Beyond intelligent mobility systems, the findings offer direct implications for educational environments, particularly in smart campuses and learning analytics. ML-based human trajectory prediction can support student flow optimization, campus safety monitoring, and adaptive learning space design. This study therefore provides a conceptual and practical foundation for integrating predictive mobility analytics into educational and institutional decision-making.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Review Article
EURASIA J Math Sci Tech Ed, Volume 22, Issue 4, April 2026, Article No: em2817
https://doi.org/10.29333/ejmste/18280
Publication date: 01 Apr 2026
Online publication date: 31 Mar 2026
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Article Downloads: 7
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