EVALUATING AND RANKING SERVICE QUALITY ATTRIBUTES FOR OPTIMIZING METRO RAIL TRANSIT SRVICES IN DHAKA USING ADVANCED ANALYTICS - Khan, A.F.S. Ahad Rahman; Tabassum, Nafisa; Prova, Faria Afrin; Afrin, Tasnia; Hadiuzzaman, Md. (Journal of Applied Engineering Science, 2025)
As Dhaka embraces its first Mass Rapid Transit (MRT) system, understanding what drives users’ satisfaction has become crucial for shaping its success. This study examines the service quality (SQ) of MRT Line-6, identifying and prioritizing the factors that influence user perceptions of this transformative transit solution. Using advanced machine learning models such as Random Forest Classifier, Support Vector Machine, and CART, alongside statistical methods like Probit, Ordered and Multiple Linear Regression, the research provides a robust analysis of twenty-nine (29) SQ indicators. Random Forest Classifier emerged as the most effective model, achieving 88.21% accuracy and offering valuable insights into the interrelationships of key attributes, including inclusive service performance, customer loyalty, switching cost from other transportation mode, ticket affordability, performance of ticketing system, and feeder service costs. Survey results highlight that most users around 60% switched from buses to MRT for its comfort and reliability. The findings emphasize the need to enhance affordability, accessibility, overall comfort and feeder services while promoting digital ticketing through options like Rapid Pass and online platforms. These actionable insights offer a roadmap for policymakers and urban planners to optimize MRT services, aligning with global best practices to support sustainable urban mobility in Dhaka and beyond.
Keywords: service quality, urban transportation, machine learning models, attribute prioritization, passenger experience