A Machine Learning – Based Study on Customer Satisfaction with Public and Private Transport in Guruvayoor Municipality

Transportation systems play a crucial role in urban development, impacting accessibility, convenience, and economic efficiency. This study applies machine learning techniques to analyze customer satisfaction with public and private transportation in Guruvayoor Municipality. Using a structured survey and data collected from 260 respondents, key satisfaction factors such as service quality, affordability, availability, and digital integration were evaluated. Machine learning models, including regression and classification algorithms, were applied to predict satisfaction levels and identify key factors influencing passenger experience. The study found that public transport is more affordable but struggles with inconsistent schedules, overcrowding, and lack of digital services. Private transportation, on the other hand, offers greater flexibility but at a higher cost. The research highlights the benefits of predictive analytics in optimizing transport services, enhancing user satisfaction, and improving operational efficiency. The study also discusses the potential for integrating AI-driven solutions such as real-time tracking, demand forecasting, and personalized commuting experiences.