SHAPEMOV aimed to develop a simulation platform for shared mobility systems to facilitate the planning and management of services by operators, as well as the design of regulatory frameworks by authorities.
InPercept aims to develop enabling technologies that allow autonomous vehicles to operate with higher levels of efficiency and safety. These technologies will help the vehicles to detect obstacles and adverse conditions and to react accordingly.
The main goal of SHAPE was to enhance the prediction models for shared mobility system demand and to develop new functionalities for the simulation platform of these services.
AVENUE is an AI4Cities project that aimed to develop an AI-based decision support tool for designing and monitoring shared mobility regulatory frameworks oriented towards the reduction of GHG emissions.
The goal of SIMBAD was to develop and evaluate a set of machine learning approaches aimed at providing state-of-the-art ATM microsimulation models with the level of reliability, tractability and interpretability required to effectively support performance evaluation at ECAC level.
AICHAIN aimed to enhance ATM systems by articulating an advanced privacy-preserving federated learning architecture in which neither the training data nor the training model need to be exposed thanks to the combination of two emerging technologies: FedML and blockchain technologies.
MOMENTUM aimed at developing a set of new data analysis methods, transport models, and planning support tools to capture the impact of new transport options on the urban mobility ecosystem, in order to support cities in the task of designing the right policy mix to exploit the full potential of these emerging mobility solutions.
BD4PT developed a new technology able to process data collected from smart payment systems and combine it with other data sources in order to analyse travel behaviour, generate mobility indicators and assist public transport authorities and operators in the planning of public transport systems.
BigData4ATM is a research project within SESAR 2020 Exploratory Research which investigated how new sources of passenger-centric data coming from smart personal devices can be analysed to extract relevant information about passengers’ behaviour and how this information can be used to inform ATM decision making processes.
BEACON studied the feasibility of extending UDPP (User-Driven Prioritisation Process) to allow multi-prioritisation processes in the airspace (e.g. encompassing departure slots, regulation slots, arrival manager slots), and exchange of slots between airlines.
ACCESS was a project within SESAR WPE Long Term and Innovative Research which addressed airport slot allocation from the perspective of complex adaptive systems. The project developed an agent-based model of the air transport network that was used to evaluate different market-based mechanisms for airport slot allocation.
INTUIT explored the potential of a variety of visual analytics and machine learning techniques to improve our understanding of the trade-offs between ATM KPAs, identify cause-effect relationships between performance drivers and performance indicators at different scales, and develop new decision support tools for ATM performance monitoring and management.
