Smart City Technical Planning Based on Time Series Forecasting of IOT Data

The research focuses on smart city technical planning based on IoT data time series forecasting. The study use machine learning techniques, notably random forest regression and decision tree regression, to anticipate the number of vehicles at various smart city intersections. The forecasting models are built using past IoT data obtained from municipal sensors. To prepare the data for model training and evaluation, data preprocessing techniques such as feature extraction and normalisation are used. Metrics such as mean absolute error, mean absolute percentage error, and R2 score are used to evaluate the models' performance. The results are visualised using graphs and tables to provide insights into the forecasting models' accuracy and efficacy. This study's findings contribute to the subject of smart city planning by presenting a data-driven strategy to anticipating traffic patterns and optimising urban infrastructure.