Researchers Rui Yao, Xinyu Ma, and Kenan Zhang from the University of Arizona have developed a novel model to understand and optimize Mobility-as-a-Service (MaaS) systems, a concept that is gaining traction in the energy and transportation sectors. Their work, published in the journal Transportation Research Part B, focuses on the intricate interactions between MaaS platforms, service operators, and travelers, aiming to create a mutually beneficial scenario for all parties involved.
The study models a MaaS system as a multi-leader-multi-follower game, where the MaaS platform, service operators, and travelers interact in a coopetitive environment. In this setup, the MaaS platform purchases service capacity from operators and sells multi-modal trips to travelers based on origin-destination pricing. Meanwhile, service operators use their remaining capacities to serve single-modal trips. Travelers, acting as followers, choose their modes of transportation and routes based on prices and congestion levels.
To simplify these complex interactions, the researchers introduced a virtual traffic operator and proposed a single-level variational inequality (VI) formulation. This approach allows for parallel solution procedures, making it suitable for large-scale applications. The team proved that an equilibrium solution always exists given the negotiated wholesale price of service capacity.
Numerical experiments on small and extended multi-modal networks demonstrated that the wholesale price can be adjusted to align with various system-wide objectives. The results showed that the proposed MaaS system can create a “win-win-win” outcome, where service operators and travelers benefit compared to scenarios without MaaS, while the MaaS platform remains profitable. This Pareto-improving regime can be explicitly specified with the wholesale capacity price.
For the energy sector, this research highlights the potential of MaaS systems to optimize transportation networks, reduce congestion, and improve overall efficiency. By integrating multi-modal trips and considering the complex interactions between different stakeholders, MaaS platforms can contribute to more sustainable and energy-efficient urban mobility solutions. The proposed model and solution algorithm provide a scalable framework for implementing and optimizing MaaS systems in real-world scenarios.
This article is based on research available at arXiv.