Coordinated scheduling and pricing for public transport-oriented MaaS systems

Abstract

This paper studies a coordinated service planning problem for public transport-oriented Mobility-as-a-Service (MaaS) systems under time-varying passenger demand. We consider the integrated optimization of schedules, vehicle compositions, stop patterns, pricing, the rebalancing strategy of modular units, and passenger routing in a multi-modal public transport network with metro and modular bus services. A public transport-oriented MaaS platform is modeled as a planning and coordination tool that recommends scheduling and pricing decisions to operators, rather than directly operating services or setting fares. To capture the interaction between supply-side service design and demand-side time-dependent passenger routing, we formulate a bi-objective mixed-integer nonlinear programming model that balances public welfare and financial sustainability. The model is reformulated as a single-objective optimization formulation via the ε-constraint method, and solved using a hybrid algorithm that combines Adaptive Large Neighborhood Search (ALNS) with GUROBI. Computational experiments on both small-scale and real-world instances demonstrate the effectiveness of the proposed approaches in supporting scalable, coordinated, and sustainable public transport planning within the MaaS framework and provide managerial insights.