Abstract: The rapid evolution of urban environments necessitates the implementation of sophisticated paradigms such as the 'Smart City' to mitigate the adverse effects of hyper-urbanization. This paper evaluates the synergy between Intelligent Transport Systems (ITS) and the Internet of Things (IoT) as a cornerstone for sustainable mobility and enhanced urban quality of life. Unlike conventional traffic management, our approach focuses on the deployment of decentralized intelligent architectures and Multi-Agent Systems (MAS) to facilitate real-time data processing. Through the application of reinforcement learning techniques, specifically Q-learning algorithms, the study demonstrates a significant optimization of traffic flows, leading to a measurable reduction in CO2 emissions and energy consumption. The findings validate the 'green kilometers in motion' framework, illustrating how 'Agents of Things' can transform urban infrastructure into a self-adaptive ecosystem. The results provide a robust empirical basis for integrating advanced IoT-driven solutions into modern urban planning strategies to achieve environmental and economic resilience.

Keywords: Smart Cities, Intelligent Mobility, Intelligent Transport Systems (ITS), Internet of Things (IoT), Machine Learning, Sustainable Development, Green Kilometers

Pages: 39-46

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