An AI-Based Approach for Real-Time Traffic Prediction in Smart Cities

The rapid growth of urban populations has led to increased traffic congestion, posing significant challenges for city planners and commuters. Smart cities aim to address these challenges through intelligent systems that enhance the efficiency and sustainability of urban infrastructure. This paper presents an AI-based approach for real-time traffic prediction, focusing on the integration of machine learning (ML) and deep learning (DL) techniques to process dynamic traffic data in real-time environments.

Our proposed system utilizes live traffic feeds collected from GPS devices, roadside IoT sensors, and traffic surveillance cameras to analyze patterns and forecast congestion hotspots. A hybrid deep learning architecture combining Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) is employed to handle both temporal and spatial data effectively. The LSTM model captures time-based dependencies in traffic flow, while the CNN component extracts spatial features such as road topology and vehicle density.

Experimental results using datasets from three major metropolitan cities—Delhi, Beijing, and Barcelona—demonstrate that our model achieves higher accuracy and lower latency compared to traditional statistical methods and standalone ML algorithms. The system adapts to sudden changes in traffic conditions, such as accidents or weather disruptions, ensuring reliable predictions.

The findings indicate significant improvements in traffic flow optimization, route planning, and emergency response coordination. Moreover, the framework is scalable and can be integrated with existing traffic management systems in smart cities. Future enhancements may include incorporating multimodal transportation data (e.g., buses, metros), real-time weather updates, and reinforcement learning for continuous optimization.