Rolul Machine Learning în Smart Cities

Abstract

The objective of this study is to explore the role of Machine Learning (ML) in enhancing the functionality and sustainability of smart cities. Machine learning (ML) plays a crucial role in smart cities by enabling data-driven insights and automation that make urban management more efficient, sustainable, and responsive to residents' needs. Prior work has established that ML algorithms can significantly improve urban planning, traffic management, environmental monitoring, and public safety. Prior work in this field which we consider relevant are Chaturvedi and Vries Chaturvedi & Vries (2021) who highlight the effectiveness of ML in urban land use planning, and Nosratabadi et al. Nosratabadi et al. (2020) who provide a comprehensive survey of ML models tailored for urban sustainability. Our approach synthesizes these findings to illustrate how ML can be used to address urban challenges through data analysis. The implications of integrating ML into smart city frameworks are profound. By utilizing real-time data from various sources, including IoT devices and social media, cities can enhance their responsiveness to dynamic urban conditions. Also, ML models can analyze environmental data to monitor air quality and urban greenness, contributing to public health and ecological sustainability. The value of ML in smart cities lies in its ability to transform vast datasets into insights, thus helping decidents to make informed decision-making and to efficiently allocate their resources. As cities continue to evolve, the adoption of advanced ML techniques will be crucial for developing resilient urban environments. This study underscores the necessity for ongoing research to use the potential of machine learning in shaping urban living, but with an important accent on its ethical applications.