Collaborative Game-Theoretic Optimization of Public Transport Fare Policies: A Global Framework for Sustainable Urban Mobility

Abstract

Urbanization intensifies the need for sustainable public transportation that balances financial viability, environmental sustainability, and social equity. Traditional fare-setting methods often focus narrowly on financial objectives, neglecting broader impacts. This study introduces a novel collaborative game-theoretic model integrating user sentiment analysis to optimize fare policies. By incorporating utilities of passengers, operators, and governments, and employing the Shapley value for fair benefit distribution, this model aims to maximize social welfare. The methodology frames fare optimization as a cooperative game among stakeholders, integrating passenger preferences through sentiment analysis. The social welfare function combines the utilities of all stakeholders and is maximized under operational, environmental, and financial constraints. Implemented in Python and applied to Isparta, Turkey, the model identifies an optimal fare of 19.5 TL (ranged between 14 and 26.50 TL) that maximizes social welfare, aligning closely with existing fares. Shapley value analysis distributes the benefits, assigning 221,457 (35.6%) units to passengers, 54,562 (8.7%) units to operators, and 347,433 (55.7%) units to the government, highlighting significant environmental gains for the government. Sensitivity analyses confirm the model’s robustness across varying trip volumes, suggesting its applicability to diverse urban settings. This research contributes to socially equitable and user-centric fare policies by providing a comprehensive framework aligning stakeholder interests. Policymakers can leverage this model to design fare strategies promoting sustainability, efficiency, and collaboration in public transportation systems.