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Investigating the Users’ Perception of Metro Rail Service Quality with Machine Learning Approach and Explainable Artificial Intelligence—Case Study: Dhaka, Bangladesh

Ashraf Uddin Fahim & Masaaki Minami

Environment and Urbanization Asia2025https://doi.org/10.1177/09754253251331934article
ABDC C
Weight
0.50

What the paper says

Users of public transit have diverse views on service quality. In Dhaka, travelling by metro rail service has played a significant role in people’s transportation ever since it was established in this city in 2022. This survey examined metro riders’ satisfaction with station, ticketing, security, information, platform, employee and metro rail services. A purposeful sample strategy was utilized to collect 382 fully screened surveys on Dhaka Metro Users' satisfaction. Data were analysed using machine learning and explainable AI. IBM SPSS 25 statistical software performed some statistical analysis, like chi-square tests and frequencies, whereas Python and Google Colab used machine learning models. Metro rail services were found satisfactory by 46.7% men and 36.2% women. Machine learning models found that in terms of quality, station-related services ranked highest, whereas platform, ticketing and security services had the least impact. Maximum service quality prediction accuracy was 84% with the Random Forest model. To meet metro users’ needs, the most essential variable will be used to improve services. This study shows how machine learning and explainable AI (using SHAP values) can discover transport service elements for improvement.

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https://doi.org/https://doi.org/10.1177/09754253251331934

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@article{ashraf2025,
  title        = {{Investigating the Users’ Perception of Metro Rail Service Quality with Machine Learning Approach and Explainable Artificial Intelligence—Case Study: Dhaka, Bangladesh}},
  author       = {Ashraf Uddin Fahim & Masaaki Minami},
  journal      = {Environment and Urbanization Asia},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/09754253251331934},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.