Digital Twin of Parking Space

Abstract

Increasing urbanization and motorization lead to a shortage of parking spaces, resulting in congestion, increased emissions, and a declining quality of life. Traditional parking management methods are ineffective in addressing this issue, necessitating the use of data analysis and forecasting tools. This paper examines the use of a digital twin of the Kazan parking system. Data was filtered and integrated, points of interest were clustered, and a correlation analysis of factors influencing parking occupancy was performed. Linear regression, decision tree, random forest, XGBoost, MLP, and LSTM models were trained and compared to predict occupancy levels. The random forest model demonstrated the best results. The developed digital twin prototype enables monitoring and scenario modeling, making it an effective tool for parking space optimization and management decision-making.