AI-Driven Optimization for Efficient Public Bus Operations

Public transport bus services often experience financial inefficiencies due to high operational costs and unbalanced service allocation. To address these challenges, this study presents a machine learning-based framework aimed at optimizing financial and operational performance in public bus systems. A dataset comprising 57 routes including cost, service, and ridership data was analyzed to identify key factors correlated with net revenue. These features were integrated into multiple predictive models, among which support vector regression (SVR) with a Gaussian kernel and Bayesian optimization achieved the highest accuracy (R 2 = 0.99), indicating excellent generalization capability. Scenario simulations using the trained SVR model evaluated the effects of service and cost adjustments. Results showed that cutting personnel costs had the most significant effect on net income, followed by administrative and financial expenses. These findings highlight the importance of data-driven strategies such as route reallocation and workforce optimization. The proposed framework offers transit agencies a robust tool for improving efficiency and ensuring financial sustainability.