Imagine if getting to the airport was as smooth as a choreographed dance, with self-driving cars and air taxis perfectly timed to meet you. This work creates a digital brain that predicts how people will travel and where to put landing pads for flying taxis. It helps airports avoid traffic jams by smartly directing autonomous fleets during delays.
Airports struggle with ground congestion and inefficient passenger access. Current systems cannot effectively integrate emerging technologies like self-driving cars and air taxis into their operational planning.
A suite of three AI tools: MAIA-Engine for predicting passenger behavior, MAIA-CCAM for autonomous vehicle dispatching, and MAIA-UAM for air taxi landing site selection.
If you are an airport operator dealing with ground congestion and outdated access points — this project developed MAIA-UAM that recommends the best locations for vertiports to balance passenger experience and operational limits.
If you are a fleet operator dealing with unpredictable passenger demand and traffic disruptions — this project developed MAIA-CCAM that optimizes vehicle dispatching to keep services reliable.
If you are an air taxi provider dealing with uncertainty about where to land in a city to maximize airport trips — this project developed a site selection tool to improve competitiveness and sustainability.
Based on available project data, no specific commercial pricing or cost per license is mentioned; the project received a EUR 1,000,000 EU contribution for development.
The tools are designed for the European airport network and are tested through a set of case studies to demonstrate their capabilities across different locations.
Based on available project data, the specific IP and licensing agreements are not detailed, though the project is coordinated by Nommon Solutions and Technologies SL.
The project uses digital twins and AI tools to integrate autonomous vehicle and UAV services with existing airport infrastructure and traditional transport modes.
The project vision anticipates that these integrated services will be available within 5-10 years.
The consortium is highly commercially oriented, with a 50% industry ratio consisting of 6 partners across 4 countries. The presence of 4 SMEs suggests a focus on agile development and potential for rapid commercialization of the 16 deliverables, supported by one university and one research center for technical validation.
Contact NOMMON SOLUTIONS AND TECHNOLOGIES SL in Spain
Contact us to explore licensing opportunities for the MAIA-Engine toolset.
