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To address the growing need for advanced tools that enable urban policymakers to conduct comprehensive cost-benefit analyses of traffic management changes, the Urbanite H2020 project has developed innovative artificial intelligence methods. Among them is a robust decision support system that assists policymakers in evaluating and selecting optimal urban mobility planning modifications by combining objective and subjective criteria. Utilising open-source microscopic traffic simulation tools, accurate digital models (or “digital twins”) of four pilot cities—Bilbao, Amsterdam, Helsinki, and Messina—were created, each addressing unique mobility challenges. These challenges include reducing private vehicle access in Bilbao’s city center, analysing the impact of increased bicycle traffic and population growth in Amsterdam, constructing a mobility-enhancing tunnel in Helsinki, and improving public transport connectivity in Messina. The research introduces five key innovations: the application of a consistent open-source simulation platform across diverse urban environments, addressing integration and consistency challenges; the pioneering use of Dexi for advanced decision support in smart cities; the implementation of advanced visualisations; and the integration of the machine learning tool, Orange, with a user-friendly GUI interface. These innovations collectively make complex data analysis accessible to non-technical users. By applying multi-label machine learning techniques, the decision-making process is accelerated by three orders of magnitude, significantly enhancing urban planning efficiency. The Urbanite project’s findings offer valuable insights into both anticipated and unexpected outcomes of mobility interventions, presenting a scalable, open-source AI-based framework for urban decision-makers worldwide.

We have studied the performances of (a) a two-way coupled atmosphere–ocean modeling system and (b) one-way coupled ocean model (forced by the atmosphere model), as compared to the available in situ measurements during and after a strong Adriatic bora wind event in February 2012, which led to extreme air–sea interactions. The simulations span the period between January and March 2012. The models used were ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) (4.4 km resolution) on the atmosphere side and an Adriatic setup of Princeton ocean model (POM) (1°∕30 × 1°∕30 angular resolution) on the ocean side. The atmosphere–ocean coupling was implemented using the OASIS3-MCT model coupling toolkit. Two-way coupling ocean feedback to the atmosphere is limited to sea surface temperature. We have compared modeled atmosphere–ocean fluxes and sea temperatures from both setups to platform and CTD (conductivity, temperature, and depth) measurements from three locations in the northern Adriatic. We present objective verification of 2 m atmosphere temperature forecasts using mean bias and standard deviation of errors scores from 23 meteorological stations in the eastern part of Italy. We show that turbulent fluxes from both setups differ up to 20 % during the bora but not significantly before and after the event. When compared to observations, two-way coupling ocean temperatures exhibit a 4 times lower root mean square error (RMSE) than those from one-way coupled system. Two-way coupling improves sensible heat fluxes at all stations but does not improve latent heat loss. The spatial average of the two-way coupled atmosphere component is up to 0.3 °C colder than the one-way coupled setup, which is an improvement for prognostic lead times up to 20 h. Daily spatial average of the standard deviation of air temperature errors shows 0.15 °C improvement in the case of coupled system compared to the uncoupled. Coupled and uncoupled circulations in the northern Adriatic are predominantly wind-driven and show no significant mesoscale differences.

The objective of the URBANITE project is to design an open-data, open-source, smart-city framework to enhance the decision-making processes in European cities. The framework's basis is a robust and user-friendly simulation tool that is supplemented with several innovative service modules. One of the modules, a multi-output, machine-learning unit, is deployed on the simulation results, enabling city officials to more effectively analyse vast quantities of data, discern patterns and trends, and so facilitate advanced policy decisions. The city's decision makers define potential city scenarios, key performance indicators, and a utility function, while the module assists in identifying the policy that is best aligned with the stipulated constraints and preferences. One of the main improvements is a speeding up of the policy testing for the decision makers, reducing the time needed for one policy verification from 3 hours to around 10 seconds. The system was evaluated for Bilbao's Moyua area, where it suggested strategies that could result in a decrease in emissions of more than 5% CO2, NOx, PM in the selected area and a broader part of the city with a machine-learning accuracy of 91%. The system was therefore able to provide valuable insights into effective policies for restricting private traffic in specific districts and identifying the most advantageous times for these restrictions.