| Abstrakt: |
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 aggregation of node features, thereby achieving effective modeling of urban road network traffic flow. During the model training phase, we used real traffic datasets for experimental verification and integrated various information such as time, space, and road features into the model. The experimental results show that compared to traditional methods, this research prediction method has achieved better performance in traffic flow prediction tasks, with higher prediction accuracy and robustness. It has stronger applicability and effectiveness in different traffic scenarios. By integrating multi-dimensional information and introducing attention mechanisms, this method has significant advantages in improving the accuracy and robustness of traffic flow prediction, and has important practical significance and application prospects for the construction of smart transportation systems and the development of smart cities. [ABSTRACT FROM AUTHOR] |
