MAIA
22065
From 2023 to 2025
TML collaborated on a data analysis toolbox and models developed to support informed design and implementation of multimodal airport access solutions. This toolbox drew on two innovative mobility solutions: autonomous vehicle fleets and unmanned aerial vehicle fleets.
Air transport is multimodal by nature, as every passenger using air transport services needs to combine different modes of transport to have a seamless door-to-door journey. However, the emergence of new technologies such as cooperative, connected, and automated mobility (CCAM) and urban air mobility (UAM) has great potential to improve airport accessibility, increase trip reliability and reduce environmental impacts. Therefore, the MAIA project developed a data analysis and modelling toolbox to support informed design and implementation of multimodal airport access solutions. This toolbox relied on two innovative mobility solutions: autonomous vehicle fleets and unmanned aerial vehicle fleets. The effectiveness of these solutions was demonstrated through two comprehensive case studies: Madrid Barajas Airport and Brussels Airport.
MAIA provided three tools based on CCAM and UAM technologies to optimise the implementation and operation of innovative multimodal airport access services:
The proposed research methodology was structured around five axes:
TML developed and validated the MAIA Engine by collecting data and developing algorithms for passenger profiling, airport access monitoring and demand modelling. TML researcher Behzad co-wrote a paper based on the reasearch. You can read the paper via de download section on the right.
Air transport is multimodal by nature, as every passenger using air transport services needs to combine different modes of transport to have a seamless door-to-door journey. However, the emergence of new technologies such as cooperative, connected, and automated mobility (CCAM) and urban air mobility (UAM) has great potential to improve airport accessibility, increase trip reliability and reduce environmental impacts. Therefore, the MAIA project developed a data analysis and modelling toolbox to support informed design and implementation of multimodal airport access solutions. This toolbox relied on two innovative mobility solutions: autonomous vehicle fleets and unmanned aerial vehicle fleets. The effectiveness of these solutions was demonstrated through two comprehensive case studies: Madrid Barajas Airport and Brussels Airport.
MAIA provided three tools based on CCAM and UAM technologies to optimise the implementation and operation of innovative multimodal airport access services:
- MAIA-Engine: a set of tools for passenger-centric implementation.
- MAIA-CCAM: a vehicle allocation tool to support the operation of shared autonomous vehicle fleets.
- MAIA-UAM: a framework for selecting suitable vertiport locations for unmanned aerial vehicle services.
The proposed research methodology was structured around five axes:
- Axis 1: Problem definition: the project started by characterising baseline conditions based on spatial analysis techniques.
- Axis 2: Development and validation of the MAIA Engine, including the creation of a data inventory, the use of machine learning classification models, synthetic algorithms for population generation, data-driven models for predicting demand for shared mobility and discrete choice models for airport access services.
- Axis 3 and Axis 4: Development and validation of MAIA-CCAM and MAIA-UAM, respectively, both guided by a Design Thinking approach.
- Axis 5: Cross-sectional demonstration in case studies, where MAIA modeled passenger behaviour in two specific cases using the MAIA Engine, with the aim of providing input for MAIA-CCAM and MAIA-UAM. Simulations were conducted to compare multimodal performance indicators before and after the implementation of new innovative mobility services.
TML developed and validated the MAIA Engine by collecting data and developing algorithms for passenger profiling, airport access monitoring and demand modelling. TML researcher Behzad co-wrote a paper based on the reasearch. You can read the paper via de download section on the right.
