Check out the international projects we are part of
BAMBOO developed new methodologies and algorithms to extract travel demand information from anonymised mobile network data and to integrate such information into state-of-the-art transport simulation systems and interactive visualisation tools.
CONDUCTOR’s main goal is to design, integrate and demonstrate advanced, high-level traffic and fleet management systems that enable efficient and globally optimal transportation of passengers and goods, while ensuring seamless multimodality and interoperability.
DIGITWIN4CIUE aims to train the future leaders of a digitally transformed civil engineering sector through an international joint master’s degree programme on the application of digital twins to civil engineering and through a Centre of Excellence that accelerates the digital transformation of the civil engineering sector in Europe.
The PASSPORT project aims to improve the planning and management processes for urban and interurban public transport, by providing tools that enable the optimisation of transport services based on the expected behaviour of demand.
SOTERIA aims to accelerate the achievement of the Vision Zero EU goal through a holistic framework of innovative models, tools and services that enable data driven urban safety intelligence, facilitate safe travelling of vulnerable road users, and foster the safe integration of micromobility services in complex environments.
The main goal of TravelInt is to develop a set of big data and machine learning technologies that allow to obtain detailed information about the passengers behaviour and support the airport planning and management decision-making process.
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.
