Traffic Prediction and Optimization with Deep Learning based on a Vehicle–Road–Cloud Integration Platform

Abstract

Traffic flow prediction and planning control can effectively improve traffic efficiency, which is a current research hotspot. Many existing studies mainly rely on traditional single-component sensors and static traffic models, which cannot fully adapt to dynamic and unpredictable traffic scenarios. These approaches often fail to accommodate the real-time variability of urban traffic, which lacks holistic, dynamic systems that integrate real-time data and predictive models to enhance traffic efficiency and safety. We address these limitations by introducing an integrated approach leveraging deep learning techniques and traffic management strategies. By combining real-time data analysis and predictive modeling, the platform facilitates smoother traffic operations and reduces congestion. Advanced reinforcement learning dynamically adjusts traffic signals according to the operational conditions, optimizing vehicle flow across intersections. This study develops a vehicle–road–cloud integration platform, which integrates communication and operational technologies across vehicle end-points, roadside infrastructure, and cloud-based computing resources. The platform enhances traffic efficiency, safety, and manageability by leveraging the capabilities of each component. Real traffic data is collected by vehicle sensors and roadside infrastructure and transmitted to the cloud for prediction and optimization. A graph convolutional network combined with a transformer model predicts traffic flows. To improve traffic efficiency, prediction results are used to optimize traffic light control through deep reinforcement learning, thereby minimizing delays and improving overall travel times. This adaptive, scalable platform addresses existing knowledge gaps and tackles the challenges of modern urban traffic management, ultimately aiming to create a safer and more efficient transportation environment.