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A Comprehensive Analysis of Clustering Public Utility Bus Passenger’s Behavior during the COVID-19 Pandemic: Utilization of Machine Learning with Metaheuristic Algorithm

Author

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  • Maela Madel L. Cahigas

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Ferani E. Zulvia

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Ardvin Kester S. Ong

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Yogi Tri Prasetyo

    (International Bachelor Program in Engineering, Yuan Ze University, 135 Yuan-Tung Rd., Chung-Li 32003, Taiwan
    Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Rd., Chung-Li 32003, Taiwan)

Abstract

Public utility bus (PUB) systems and passenger behaviors drastically changed during the COVID-19 pandemic. This study assessed the clustered behavior of 505 PUB passengers using feature selection, K-means clustering, and particle swarm optimization (PSO). The wrapper method was seen to be the best among the six feature selection techniques through recursive feature selection with a 90% training set and a 10% testing set. It was revealed that this technique produced 26 optimal feature subsets. These features were then fed into K-means clustering and PSO to find PUB passengers’ clusters. The algorithm was tested using 12 different parameter settings to find the best outcome. As a result, the optimal parameter combination produced 23 clusters. Utilizing the Pareto analysis, the study only considered the vital clusters. Specifically, five vital clusters were found to have comprehensive similarities in demographics and feature responses. The PUB stakeholders could use the cluster findings as a benchmark to improve the current system.

Suggested Citation

  • Maela Madel L. Cahigas & Ferani E. Zulvia & Ardvin Kester S. Ong & Yogi Tri Prasetyo, 2023. "A Comprehensive Analysis of Clustering Public Utility Bus Passenger’s Behavior during the COVID-19 Pandemic: Utilization of Machine Learning with Metaheuristic Algorithm," Sustainability, MDPI, vol. 15(9), pages 1-31, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7410-:d:1136985
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    References listed on IDEAS

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    Cited by:

    1. Maela Madel L. Cahigas & Ardvin Kester S. Ong & Yogi Tri Prasetyo, 2023. "Super Typhoon Rai’s Impacts on Siargao Tourism: Deciphering Tourists’ Revisit Intentions through Machine-Learning Algorithms," Sustainability, MDPI, vol. 15(11), pages 1-29, May.
    2. Paula Zeah N. Bautista & Maela Madel L. Cahigas, 2024. "Exploring Employee Retention among Generation Z Engineers in the Philippines Using Machine Learning Techniques," Sustainability, MDPI, vol. 16(12), pages 1-21, June.

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