Periodic Reporting for period 1 - MAIA (Multimodal Access for Intelligent Airports)
Reporting period: 2023-06-01 to 2024-05-31
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.
This goal entails five specific objectives, which are further developed below (together with their associated expected results):
- Identify the opportunities and risks associated with passenger mobility innovations in a multimodal airport access context.
- Develop MAIA-Engine (Solution 1), 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 (Solution 2), a vehicle dispatching tool to support the operation of Shared Autonomous Vehicle (SAV) fleets in the airport access, able to mitigate multimodal disruption impacts.
- Develop MAIA-UAM (Solution 3), a vertiport site selection framework to support the implementation of Unmanned Aerial Vehicles (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.
Additionally, MAIA outcomes will generate scientific, societal and economic/technological wider project impacts that can be mapped to the impacts specified in the work programme, i.e. high-level outcomes and performance objectives established by the SRIA (SESAR JU, 2020a) for each Flagship, particularly for Flagship 6 (Multimodality and passenger experience). Ultimately, the achievement of these impacts helps the realisation of the Digital European Sky (DES) vision.
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.
This goal entails five specific objectives, which are further developed below (together with their associated expected results):
- Identify the opportunities and risks associated with passenger mobility innovations in a multimodal airport access context.
- Develop MAIA-Engine (Solution 1), 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 (Solution 2), a vehicle dispatching tool to support the operation of Shared Autonomous Vehicle (SAV) fleets in the airport access, able to mitigate multimodal disruption impacts.
- Develop MAIA-UAM (Solution 3), a vertiport site selection framework to support the implementation of Unmanned Aerial Vehicles (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.
Additionally, MAIA outcomes will generate scientific, societal and economic/technological wider project impacts that can be mapped to the impacts specified in the work programme, i.e. high-level outcomes and performance objectives established by the SRIA (SESAR JU, 2020a) for each Flagship, particularly for Flagship 6 (Multimodality and passenger experience). Ultimately, the achievement of these impacts helps the realisation of the Digital European Sky (DES) vision.
The activities performed and the main project achievements so far are the following:
- Spatial analysis of the accessibility of different airports across the European network aimed to identify the associated challenges.
- Desk research covering both academic papers and industrial reports regarding the analysis of the accessibility of different European airports and the identification of preliminary opportunities and challenges for airport accessibility, especially those related to UAM and CCAM services.
- Design of a stakeholder involvement process, based on the results of the spatial analysis and the desk research, aimed to develop MAIA’s conceptual framework. To this end, a Delphi poll with two rounds was implemented, engaging members from the External Experts Advisory Board (EEAB) and other external experts.
- Consolidation of the previous information in a document that describes the accessibility conditions and challenges of European airports identified in the project, the risk and opportunities of mobility innovations for facing such challenges and the high-level requirements for tools that help aviation to take the most of such opportunities.
- Definition of the methodology and development of advanced algorithms for the creation and implementation of MAIA-Engine, MAIA-CCAM and MAIA-UAM solutions
- Definition of the validation exercises to be conducted for the evaluation and demonstration of the MAIA-Engine, MAIA-CCAM and MAIA-UAM solutions
- Launch of the technical activities for the design, development, and implementation of the MAIA solutions, including data collection, algorithm implementation, and validation.
- Definition of the case studies to be conducted within the project, aimed at demonstrating the benefits and effectiveness of the different MAIA solutions
- Spatial analysis of the accessibility of different airports across the European network aimed to identify the associated challenges.
- Desk research covering both academic papers and industrial reports regarding the analysis of the accessibility of different European airports and the identification of preliminary opportunities and challenges for airport accessibility, especially those related to UAM and CCAM services.
- Design of a stakeholder involvement process, based on the results of the spatial analysis and the desk research, aimed to develop MAIA’s conceptual framework. To this end, a Delphi poll with two rounds was implemented, engaging members from the External Experts Advisory Board (EEAB) and other external experts.
- Consolidation of the previous information in a document that describes the accessibility conditions and challenges of European airports identified in the project, the risk and opportunities of mobility innovations for facing such challenges and the high-level requirements for tools that help aviation to take the most of such opportunities.
- Definition of the methodology and development of advanced algorithms for the creation and implementation of MAIA-Engine, MAIA-CCAM and MAIA-UAM solutions
- Definition of the validation exercises to be conducted for the evaluation and demonstration of the MAIA-Engine, MAIA-CCAM and MAIA-UAM solutions
- Launch of the technical activities for the design, development, and implementation of the MAIA solutions, including data collection, algorithm implementation, and validation.
- Definition of the case studies to be conducted within the project, aimed at demonstrating the benefits and effectiveness of the different MAIA solutions
MAIA will advance the state-of-the-art regarding three main areas: passenger behaviour modelling, operation of SAV in the airport context, and vertiport location intelligence.
MAIA will advance the state-of-the-art of passenger behaviour modelling by:
- Extending current methods to enrich mobile network data with passenger surveys to estimate attributes of passengers such as group travelling and luggage carrying.
- Refining current frameworks for population synthesis to exploit mobile network data and include new agent 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 and assignment models that consider new access options.
MAIA will advance the state-of-the-art of the operation of SAV in the airport context by developing fleet dispatching algorithms that:
- Take into account reliability aspects, such as guarantees and restrictions on pick-up and drop-off times (exploiting data sources such as flight schedules and evaluating passenger reactions to each fleet dispatching strategy using MAIA-Engine).
- Consider passenger attributes (e.g. group travelling, luggage carrying) in the dispatching strategies, to better account for differences in behaviour that can be exploited by mode choice models.
MAIA will advance the state-of-the-art of vertiport location intelligence by:
- Proposing a site evaluation framework that considers demand, operational, societal and environmental aspects and tailored to airport access services.
- Integrating the fine-grained demand indicators provided by MAIA-Engine in the recommendations for vertiport location across the catchment area.
- Balancing passenger experience criteria (e.g. ability to reduce door-to-door travel times, walking distances in the airport) and operational constraints (e.g. operations in a co-shared space with aircraft operations) when analysing vertiport locations in the airport context.
MAIA will advance the state-of-the-art of passenger behaviour modelling by:
- Extending current methods to enrich mobile network data with passenger surveys to estimate attributes of passengers such as group travelling and luggage carrying.
- Refining current frameworks for population synthesis to exploit mobile network data and include new agent 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 and assignment models that consider new access options.
MAIA will advance the state-of-the-art of the operation of SAV in the airport context by developing fleet dispatching algorithms that:
- Take into account reliability aspects, such as guarantees and restrictions on pick-up and drop-off times (exploiting data sources such as flight schedules and evaluating passenger reactions to each fleet dispatching strategy using MAIA-Engine).
- Consider passenger attributes (e.g. group travelling, luggage carrying) in the dispatching strategies, to better account for differences in behaviour that can be exploited by mode choice models.
MAIA will advance the state-of-the-art of vertiport location intelligence by:
- Proposing a site evaluation framework that considers demand, operational, societal and environmental aspects and tailored to airport access services.
- Integrating the fine-grained demand indicators provided by MAIA-Engine in the recommendations for vertiport location across the catchment area.
- Balancing passenger experience criteria (e.g. ability to reduce door-to-door travel times, walking distances in the airport) and operational constraints (e.g. operations in a co-shared space with aircraft operations) when analysing vertiport locations in the airport context.