Assessment of Service Quality and Trust of E-Public Transportation in Doha Qatar

Abstract

This study examined the relationship between service quality, trust, and passenger satisfaction in sustainable e-public transportation, using the Doha Metro in Qatar as a case study. Despite its advanced automation, the metro faces low adoption, with less than 30% of the economically active population utilizing it. To address this, this study integrated the Social Exchange Theory (SET) and the SERVQUAL RATER model with machine learning techniques to assess commuter perceptions and satisfaction. Neural network and Long Short-Term Memory (LSTM) models outperformed traditional statistical methods, offering enhanced predictive accuracy. Based on the 319 survey responses, key service quality factors were identified, emphasizing customer experience (NI = 100.0%), security (NI = 99.9%), and service reliability (NI = 90.8%). Findings suggested that improving affordability and dynamic pricing could increase metro ridership while reducing private vehicle reliance. Additionally, predictive maintenance and crisis management strategies are recommended to enhance service reliability. This study contributes to sustainable urban mobility by providing data-driven recommendations for efficient and environmentally friendly e-public transportation. Policymakers and urban planners can utilize these insights to improve commuter satisfaction and transit system adoption. Future research may explore multi-city comparisons and hybrid modeling techniques for further refinement.

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