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With the acceleration of urbanization and the continuous growth of transportation demand, the traffic management of smart city road networks has become increasingly complex and critical. Traffic flow prediction, as an important component of smart transportation systems, is of great significance for optimizing traffic planning and improving traffic efficiency. The study collected and preprocessed traffic data in the smart city road network, including multi-dimensional information such as traffic flow, road conditions, and meteorological data. Then, based on the idea of graph neural networks, we constructed the topological structure of the urban road network and abstracted elements such as roads and intersections into nodes, using edges to represent their connection relationships, thus forming a graph dataset. Next, we introduced an attention mechanism to extract more representative node features through the weighted …