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Sustainability
Volume 15
Issue 9
10.3390/su15097410
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Article

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

by
Maela Madel L. Cahigas
1,2,*,
Ferani E. Zulvia
1,
Ardvin Kester S. Ong
1 and
Yogi Tri Prasetyo
3,4
1
School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
2
School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
3
International Bachelor Program in Engineering, Yuan Ze University, 135 Yuan-Tung Rd., Chung-Li 32003, Taiwan
4
Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Rd., Chung-Li 32003, Taiwan
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(9), 7410; https://doi.org/10.3390/su15097410
Submission received: 28 March 2023 / Revised: 22 April 2023 / Accepted: 26 April 2023 / Published: 29 April 2023
(This article belongs to the Section Sustainable Transportation)
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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.
Keywords: public utility bus (PUB); passenger; feature selection; k-means clustering; particle swarm optimization (PSO) public utility bus (PUB); passenger; feature selection; k-means clustering; particle swarm optimization (PSO)

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MDPI and ACS Style

Cahigas, M.M.L.; Zulvia, F.E.; Ong, A.K.S.; Prasetyo, Y.T. A Comprehensive Analysis of Clustering Public Utility Bus Passenger’s Behavior during the COVID-19 Pandemic: Utilization of Machine Learning with Metaheuristic Algorithm. Sustainability 2023, 15, 7410. https://doi.org/10.3390/su15097410

AMA Style

Cahigas MML, Zulvia FE, Ong AKS, Prasetyo YT. A Comprehensive Analysis of Clustering Public Utility Bus Passenger’s Behavior during the COVID-19 Pandemic: Utilization of Machine Learning with Metaheuristic Algorithm. Sustainability. 2023; 15(9):7410. https://doi.org/10.3390/su15097410

Chicago/Turabian Style

Cahigas, Maela Madel L., Ferani E. Zulvia, Ardvin Kester S. Ong, and 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 15, no. 9: 7410. https://doi.org/10.3390/su15097410

APA Style

Cahigas, M. M. L., Zulvia, F. E., Ong, A. K. S., & Prasetyo, Y. T. (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, 15(9), 7410. https://doi.org/10.3390/su15097410

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