PREDICTIVE ANALYTICS FOR OPTIMIZING URBAN AMENITIES IN SMART CITIES: A MACHINE LEARNING APPROACH

Abstract

The rapid urbanization and growing population in cities have led to an increased demand for efficient and sustainable management of urban infrastructure and amenities. Smart cities aim to leverage advanced technologies to enhance the quality of life for citizens by providing intelligent and optimized urban services. One crucial aspect of smart city development is the prediction and planning of amenities to meet the evolving needs of residents. This paper presents a machine learning-based approach for predicting smart city amenities. The proposed system utilizes historical data from various sources, such as citizen feedback, sensor networks, and government records, to train predictive models. By analyzing these datasets, the system can identify patterns and correlations to forecast the demand and utilization of different amenities in the future. The machine learning models employed in this study include regression, classification, and clustering algorithms. Regression models are used to predict the future usage patterns and resource requirements of existing amenities. Classification models enable the identification of potential areas for the development of new amenities based on demographic, socio- economic, and geographical factors. Clustering algorithms aid in identifying groups of similar cities or neighborhoods that exhibit similar amenity needs, facilitating targeted resource allocation and planning. To evaluate the effectiveness of the proposed approach, extensive experiments and case studies are conducted using real-world data from several cities. The results demonstrate the ability of the models to accurately forecast the demand for amenities, enabling city planners and administrators to make informed decisions regarding infrastructure investments, resource allocation, and policy-making. The contributions of this research extend beyond the realm of academia, as the developed system provides a practical tool for city planners, urban administrators, and policymakers to optimize the provision of amenities in smart cities. By accurately predicting future requirements, cities can proactively address the needs of their residents, enhance quality of life, and promote sustainable urban development.