Modular Public Transportation System Scheduling with Automatic Decision-Making

With the acceleration of urbanization and the sharp increase in transportation demand, traditional bus scheduling schemes are difficult to balance efficiency and economic benefits under the constraints of multiple factors. The modular public transportation system utilizes the configurable characteristics of the main module and trailer module, providing a new path for flexible resource scheduling. This paper proposes a modular public transportation scheduling model based on mixed integer linear programming (MILP) to address the profit maximization needs of operators. In line with the title, we present an optimization framework that transforms demand inputs into solver-ready schedules with minimal manual intervention. This automation enables fast reconfiguration and scalable deployment in practice without altering the core MIP formulation. The model comprehensively considers vehicle operating status, passenger demand distribution, ticket revenue, and operating costs, and characterizes boarding and alighting consistency, module connection, and separation decisions through refined constraints. Taking typical route data from a certain city as an example, the solution results show that, while maintaining a passenger satisfaction rate of not less than 85%, the model can increase operating profit by reducing cost compared to traditional scheduled departure schemes. The research results not only provide quantitative basis for the engineering practice of modular public transportation systems, but also lay the methodological foundation for the subsequent introduction of multi-objective optimization and uncertain demand analysis.