TATOOINE

keywords: Agentic AI, smart cities, digital shadow/twin, 15-minute city model, mobility, sustainability

Making cities more sustainable and smarter is an urgent global goal, as cities contribute more than 60% of greenhouse gas emissions. Traditional urban development promotes maximizing access to public transport and reducing car dependency. However, this paradigm has proven to be a health risk due to the overcrowding that high population density areas have generated, and even reliance on the car or long public transport journeys to access basic services, as highlighted by the COVID-19 pandemic. In 2016, the concept of the 15-minute city model (FMC) was introduced as a solution to re-plan cities for greater sustainability, inclusiveness, liveability, health and economic equity. This model focuses on the walkability of public spaces, transforming traditionally separate mobility and green infrastructure networks into a single system of public open spaces. The idea is to enable access to the experience of urban living within a quarter of an hour of home, on foot or by sustainable transport. The FMC aims to create self-sufficient neighborhoods with the essential functions of housing, work, commerce, healthcare, education and leisure, by decentralizing urban functions and services. Proposed smart city platforms, such as digital urban twins, can help cities not only to monitor cities in real time, but also to recommend adaptive policies and planning, and to drive more effective decision-making processes, such as improving urban governance, health and smart transport, optimizing city resources, saving energy and anticipating problems. Numerous smart city solutions are currently available on the market, developed by large companies such as Oracle or IBM. However, these platforms tend to be expensive, not very flexible, have very limited extensibility, require specialized staff for installation and maintenance, are highly dependent on the supplier and do not provide a holistic and citizen-oriented vision as advocated by the FMC model. When cities are modelled under a joint vision, it is easier to analyze in depth the underlying causes of existing and potential problems. The search for solutions to problems in the urban environment therefore requires the use of complex models that facilitate simulations to study and determine the best way to allocate resources to respond to citizens’ needs, and to anticipate the side effects of each possible solution in order to avoid unintended consequences.

The aim of this project is to develop an intelligent platform that combines different AI techniques (i.e., Agentic-AI, machine, deep and reinforcement learning), to generate intelligent services that support the analysis, evaluation and planning of the FMC model in urban environments. In this line, we will focus on synergistically improving short-range mobility and urban (re)planning of public and private services, green spaces and finally explore, following the concept of defensible space, how the design of urban space and the characteristics of the social environment could influence gender-based violence.

Principal investigators:

• Eduardo Guzmán
• María Victoria Belmonte

Researchers:

• José Luis Pérez de la Cruz
• Eva Millán
• Lawrence Mandow
• Llanos Mora
• José del Campo
• Mónica Trella
• Juan Gavilanes
• F. Javier Castellano
• Esperanza García-Vergara
• Juan Palma Borda
• Francisco Rodríguez Gómez