Multimodal Access for Intelligent Airports

Project Information

MAIA

Grant agreement ID: 101114853

DOI

10.3030/101114853

Project closed

EC signature date 23 May 2023

Start date 1 June 2023

End date 30 November 2025

Funded under

• Climate, Energy and Mobility

Total cost

€ 1 000 000,00

EU contribution

€ 1 000 000,00

Coordinated by

NOMMON SOLUTIONS AND TECHNOLOGIES SL
Spain

Periodic Reporting for period 2 - MAIA (Multimodal Access for Intelligent Airports)

Reporting period: 2024-06-01 to 2025-11-30

Summary of the context and overall objectives of the project

MAIA’s vision is that, within 5-10 years, CCAM and UAM based services will be integrated with the airport infrastructure and the traditional access modes. AI tools and digital twins will be used for anticipating the impacts of different service configurations on the airport access performance.

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 (SAV) fleets and Unmanned Aerial Vehicles (UAV) 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.

This goal entails five specific objectives:
- Identify the opportunities and risks associated with passenger mobility innovations in a multimodal airport access context.
- 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.
- Develop MAIA-CCAM, a vehicle dispatching tool to support the operation of SAV fleets in the airport access.
- Develop MAIA-UAM, a vertiport site selection framework to support the implementation of UAV services in the airport access, able to balance passenger experience criteria and UAM operational constraints.
- Demonstrate and evaluate the capabilities of MAIA-Engine through their application to a set of case studies in the European airport network, aimed at demonstrating to what extent the novel MAIA-CCAM and MAIA-UAM concepts can help improve passenger experience, capacity and environmental sustainability.

These project results will produce a series of project outcomes that can be mapped to the environment, capacity and passenger experience outcomes:
- MAIA will help aviation leverage the full potential of mobility innovations to improve airport access environmental sustainability
- MAIA will turn innovative multimodal airport access options into an aviation ally to provide more reliable and robust capacity
- MAIA will help provide door-to-door solutions tailored to the needs of each passenger through the integration of innovative multimodal airport access options.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

- Identification of challenges and opportunities on airport accessibility through a comprehensive process that includes a spatial analysis of the accessibility of different airports across the Europe; a desk research covering academic papers and industrial reports on the analysis of the accessibility of different European airports and the identification of preliminary opportunities and challenges for airport accessibility; and a stakeholder involvement process, based on the results of the spatial analysis and the desk research, aimed to develop MAIA’s conceptual framework.
- Development and validation of MAIA-Engine, a toolset for a passenger-centric design and implementation of innovative multimodal airport access services, which is composed by four components that include new methods and tools for monitoring and predicting passenger behaviour
- Development and validation of MAIA-CCAM, a vehicle dispatching tool to support the operation of CCAM fleets in the airport access. The tool is based on an open-source agent-based transport simulation framework, and is capable of estimating several CCAM service indicators (e.g. number of vehicles needed to serve the daily airport demand, passenger waiting times, etc.) under different service configurations (stop locations, maximum promised wait times and detours for the passengers, etc.).
- Development and validation of MAIA-UAM, a vertiport site selection framework to support the implementation of Unmanned Aerial Vehicles (UAV) services in the airport access. The tools is composed of two modules. One module focuses on the location of vertiports across the urban area, while the second module focuses on micro-location selection for the vertiport at the airport site.
- A demonstration of the capabilities of the MAIA solutions through their application to two case studies in Madrid Barajas and Brussels National airports. The application of MAIA-Engine demonstrated its capability to describe passenger demand accessing the airport, and to estimate the potential demand of UAM- and CCAM-based services. The MAIA-CCAM tool successfully assessed the impact of the application of CCAM services in both Madrid and Brussels. The tool demonstrated its utility to support CCAM operators to shape their service and to tailor it to the needs and references of the passengers, while ensuring an economic return that makes viable the operation of the services. Finally, the MAIA-UAM tool evaluated the potential location of vertiport both in urban areas and the airport site. The tool demonstrated its usefulness to support UAM operators to select the optimal location of the vertiports to maximise their profit, while maximising the demand captured.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

MAIA advanced the state-of-the-art in the following fields:

Passenger behaviour modelling:
- Developing methods to enrich Mobile Network Data (MND) with passenger surveys to estimate passenger attributes related to airport access behaviour.
- Adapting ML models for predicting the demand of shared mobility services to include airport-specific features (e.g. group travelling prevalence among passengers).
- Incorporating travel time reliability in discrete choice models that consider new access options.

SAV operation in the airport context:
- Developing fleet dispatching algorithms that consider reliability aspects, such as restrictions on pick-up and drop-off times (exploiting data sources such as flight schedules and evaluating passenger reactions to each fleet dispatching strategy), and passenger attributes (e.g. group travelling, luggage carrying) in the dispatching strategies, to better consider behavioural differences.

Vertiport location intelligence:
- Proposing a site evaluation framework tailored to airport access services that considers demand, operational, societal and environmental aspects.
- Integrating the detailed demand indicators provided by MAIA-Engine in the vertiport location process.
- Balancing passenger experience criteria (e.g. reduction of door-to-door travel times, walking distances) and operational constraints (e.g. operations co-shared with aircraft operations) when analysing vertiport locations at the airport.

MAIA Concept

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