Research on AI-Empowered Dynamic Traffic Flow Control Models and Big Data Governance Strategies for Smart City Development

Abstract

Addressing challenges in smart city traffic management, where industrial traffic generated by sectors such as manufacturing complicates flow prediction and real-time regulation, this study investigates AI-enabled dynamic traffic flow control models and big data governance strategies to improve operational responsiveness and data utilization efficiency. Building upon traffic flow theory, artificial intelligence algorithms, and big data governance frameworks, the study first establishes the conceptual foundations of AI-empowered dynamic traffic flow control and defines key evaluation dimensions, including prediction accuracy, control response speed, traffic efficiency improvement, and data governance efficiency. Subsequently, multi-source traffic data, including road surveillance, vehicle GPS, public transportation operations, and meteorological information, are collected from 10 representative smart cities to construct the multidimensional STBD-2024 dataset covering six traffic scenarios and five congestion levels. A three-dimensional framework of “Multi-source Data Fusion Governance–Intelligent Flow Forecasting–Dynamic Precision Regulation ” is then proposed to enhance data quality through cleaning, fusion, and standardization while enabling short-term traffic prediction and adaptive regulation based on enhanced deep learning models. The proposed framework also provides a scalable computational paradigm for intelligent sensing and wireless data fusion in connected transportation environments, offering methodological support for electromagnetic information acquisition and real-time perception systems in next-generation smart city infrastructures.