AI-Driven Digital Twin Framework for Intelligent Transportation Management and Infrastructure Planning in Smart Cities

Author

Mrs. Gorisetty. Nirosha, Yadla. Harika


Abstract

 Rapid urbanization and the growing number of vehicles have created significant challenges for transportation systems in smart cities, including traffic congestion, road accidents, fuel wastage, and environmental pollution. Existing navigation systems such as Google Maps provide real-time traffic updates and route guidance, but they do not support future infrastructure planning and cannot predict accidents before they occur. To address this gap, this paper proposes an AI-Driven Digital Twin framework for predictive transportation management and infrastructure planning in smart cities. The framework builds a real-time virtual model of the transportation network from data collected through IoT sensors, GPS-enabled vehicles, traffic cameras, and smart traffic signals, and combines Artificial Intelligence (AI), Machine Learning (ML), cloud computing, and Geographic Information Systems (GIS) to monitor and analyze traffic conditions continuously. Two key innovations are introduced: AI-based Future Infrastructure Planning, which recommends road widening, flyovers, underpasses, and traffic-signal improvements from long-term traffic patterns, and AI-based Accident Risk Prediction, which forecasts potential accidents from weather conditions, road status, vehicle speed, visibility, and historical accident data. LSTM and Transformer models forecast traffic conditions, Particle Swarm Optimization (PSO) performs multi-objective optimization of travel time, energy consumption, and operational cost, and a continuous feedback loop (Collect → Integrate → Predict → Simulate → Optimize → Decide → Update) keeps the twin synchronized with the physical network. The design is grounded in a systematic review of 30 research papers published between 2017 and 2026 and a comparative analysis of two case-study approaches. The framework enables proactive decision-making, improves road safety, supports sustainable urban planning, reduces congestion, and helps governments develop smarter and more efficient transportation systems.


Keywords

Digital Twin, Smart Cities, Artificial Intelligence, Machine Learning, Intelligent Transportation Systems, Infrastructure Planning, Accident Risk Prediction, IoT, LSTM, Particle Swarm Optimization, GIS.



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References


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