Saudi Cultural Missions Theses & Dissertations
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Item Restricted Scalable Human Mobility Prediction: Integrating Clustering and Parallel Processing(Saudi Digital Library, 2024) Alhomidan, Suliman; Chen, ZexunHuman mobility modelling is essential for various applications, including urban planning, transportation logistics, and public health. Traditional algorithms for predicting human movement patterns face significant computational challenges, particularly with large-scale datasets. This dissertation addresses these challenges by introducing an optimised approach that leverages parallel computing and machine learning techniques. We refactored the existing human mobility prediction algorithm to utilise Dask, a parallel computing library that enables distributed processing. This modification enhanced the algorithm's scalability and computational efficiency, making it suitable for big data environments. Additionally, we incorporated clustering as a preprocessing step to group similar users, significantly reducing the number of pairwise comparisons required for trajectory analysis. We evaluated eight clustering algorithms: K-means, Gaussian Mixture Models (GMM), DBSCAN, MeanShift, Agglomerative Clustering, OPTICS, Birch, and HDBSCAN. Each algorithm was tested with various hyperparameters and clustering approaches. Performance metrics, including execution time, Adjusted Rand Index (ARI), and Normalised Mutual Information (NMI), were used to assess the computational efficiency and clustering accuracy of each algorithm. Our findings indicate that the “mean” and “std” aggregation methods consistently provide the best performance in terms of ARI and NMI. The “std” method demonstrated the lowest execution times, highlighting its computational efficiency. The results underscore the importance of selecting appropriate clustering algorithms and parameter values to optimise performance. The improved approach was validated through practical examples, demonstrating substantial reductions in computational complexity compared to the original algorithm. For instance, clustering reduced the complexity from O(n^2∙ m^2 ) to O(t∙nk)+O(n^2/k∙m^2 ) where n is the number of users, m is the number of records per user, k is the number of clusters, and t is the number of iterations for clustering convergence. The practical implications of this research are significant, offering improved computational efficiency for applications in urban planning, public health, and commercial sectors. However, challenges such as real-time processing, adaptive clustering methodologies, and ethical considerations remain. Future research should address these challenges to further enhance the algorithm's applicability and performance. This dissertation presents a robust and scalable solution for human mobility modelling, integrating parallel computing and clustering techniques to significantly improve computational efficiency and accuracy. The flexibility of the implemented code allows users to tailor the clustering approach to their specific needs, ensuring optimal performance for various applications.6 0