MAIA · Multimodal Access for Intelligent Airports

MAIA’s vision is that, within 5-10 years, CCAM and UAM based services will be available in an integrated manner with the airport infrastructure and the remaining access modes. AI tools and digital twins will be used for anticipating the impacts of different service configurations on the performance of airports as multimodal hubs. Aviation stakeholders will be able to make evidence-based decisions on how to implement, operate and regulate novel services in a way they improve passenger experience, increase capacity and help reach sustainability goals. The goal of MAIA is to develop a set of data analytics and modelling tools to support the evidence-based design and implementation of multimodal airport access solutions based on two passenger mobility innovations: shared autonomous vehicle fleets and unmanned aerial vehicle fleets. MAIA tools will monitor and anticipate passenger behaviour changes due to these new options, optimise vehicle dispatching under multimodal disruptions and recommend appropriate locations for vertiports, with the aim of maximising the contribution of these mobility innovations to the competitiveness and sustainability of the European aviation sector. The specific objectives of MAIA are (i) identify the opportunities and risks associated with passenger mobility innovations in a multimodal airport access context; (ii) develop MAIA-Engine, a toolset for a passenger-centric design and implementation of innovative multimodal airport access services, which includes new methods and tools for predicting passenger behaviour; (iii) develop MAIA-CCAM, a vehicle dispatching tool to support the operation of Shared Autonomous Vehicle (SAV) fleets in the airport access, able to mitigate multimodal disruption impacts; (iv) develop MAIA-UAM, a vertiport site selection framework to support the implementation of Unmanned Aerial Vehicles (UAV) services in the airport access, able to balance passenger experience, capacity and environmental sustainability.