Leveraging Digital Twins for Integrated Energy and Transportation Management in Smart Cities

This paper presents an integrated framework for smart city management that combines digital twin systems, deep learning (DL) forecasting, and Particle Swarm Optimization (PSO) for multi-objective optimization. Leveraging real-time data from smart meters, transportation sensors, and urban infrastructure, the framework constructs an accurate digital twin representation to enable dynamic prediction and control. Long Short-Term Memory (LSTM) networks are employed to forecast energy consumption, grid load, and traffic congestion with high accuracy (e.g., energy prediction R² = 0.92, traffic prediction R² = 0.88). These forecasts inform the PSO module, which optimizes energy usage and travel time simultaneously through a multi-objective cost function under operational constraints. The proposed model demonstrates a 15% reduction in energy consumption and a 12% decrease in average travel time in simulation studies. Compared to traditional methods such as Genetic Algorithms (GA) and Linear Programming (LP), the PSO-based approach achieves faster convergence and more robust optimization results, reflecting its superior performance. The novelty lies in the seamless integration of digital twin predictive capabilities with PSO-driven real-time optimization, providing a scalable, efficient solution for complex urban systems. This work advances smart city technology by enabling data-driven, adaptive management of energy and transportation resources.