Resource Allocation Optimization of Smart City Based on Different ML Models

Allocating resources for charging stations is essential to support the growth of renewable energy in modern cities. It helps ensure efficient resource use and promotes the development of smarter and greener urban environments. Challenges such as limited planning methods and not enough focus on travel needs continue in current charging station planning. This uses data from smart cities about transportation networks to improve where charging stations are placed. It looks at things like building costs and how long drivers spend traveling or waiting. A convenience factor is added to make the model better, and a clustering model is created using Principal Component Analysis (PCA), with measures like Gross Domestic Product (GDP) per person population size, and car ownership to group regions and assign different levels of convenience for charging. The shortest distances between all points in the network are calculated using Voronoi diagrams, and concentrating charging demand at key traffic points. The Floyd algorithm is applied to test the efficiency of the optimized model. This solution provides a framework to move towards sustainable urban transportation.