Advanced International Journal for Research

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Volume 7 Issue 5
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Public Transportation Usage and Commuter Satisfaction Survey

Author(s) Mr. Dhruva kumar Singh
Country India
Abstract Urban public transportation systems require continuous empirical and data-driven evaluation to enhance commuter retention, optimize operational schedules, and promote sustainable municipal mobility. This research establishes an empirical, dual-layer analytical framework designed to evaluate commuter satisfaction using primary survey data (???? = 71) collected from daily transit users in Bengaluru, India. The methodological workflow bridges traditional non-parametric statistical hypothesis testing—specifically the Chi-Square Test of Independence (????2 = 18.50, ???? < 0.05, ???????? =
4)—with a Supervised Machine Learning pipeline utilizing a Random Forest Classifier (micro-????1 = 0.82). To resolve black-box ambiguity in predictive modeling, feature importances and directional contributions are extracted using SHapley Additive exPlanations (SHAP). Empirical findings demonstrate that while baseline transit adoption is driven by fare affordability, operational reliability—specifically schedule punctuality (SHAP value = +0.42) and peak-hour vehicle capacity management (SHAP value = +0.28)—serves as the primary determinant of commuter satisfaction and retention. The study concludes with a three-tier operational optimization framework tailored for municipal transit authorities.
Keywords Commuter Satisfaction; Public Transportation; Urban Mobility; Machine Learning; Random Forest; SHAP; Chi-Square Test; Bengaluru; Sustainable Transportation
Field Computer > Data / Information
Published In Volume 7, Issue 5, September-October 2026
Published On 2026-09-18
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E-ISSN 3048-7641
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