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Smart city article details
| Title | Enhancing Urban Sustainability: Developing An Open-Source Ai Framework For Smart Cities |
|---|---|
| ID_Doc | 24074 |
| Authors | Shulajkovska M.; Smerkol M.; Noveski G.; Gams M. |
| Year | 2024 |
| Published | Smart Cities, 7, 5 |
| DOI | http://dx.doi.org/10.3390/smartcities7050104 |
| Abstract | Highlights: What are the main findings? The Urbanite project developed a unified open-source simulation platform applied to four diverse European cities, effectively addressing specific mobility challenges in each city. The integration of simulation, advanced visualizations, decision support tools, and machine learning modules significantly enhances the decision-making process in urban planning. What is the implication of the main finding? Policymakers can utilize these AI-driven tools to make more informed, data-supported decisions, improving urban mobility and sustainability The methodology can be adapted to other cities, demonstrating the scalability and flexibility of the Urbanite platform in various urban settings. 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. © 2024 by the authors. |
| Author Keywords | decision support; machine learning; mobility policy; simulation; smart city |
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