Formulation and implementation of collaborative urban traffic congestion control policies

Urban traffic congestion, as an important issue restricting urban development, has been studied for the formulation and implementation of collaborative urban traffic congestion control policies. By collecting multi-source data for preprocessing, feature extraction, denoising, and dimensionality reduction, combined with CEEMDAN and improved GRU to accurately predict urban traffic congestion status, Construct a traffic flow allocation model based on genetic FW algorithm to dynamically adjust resource allocation strategies. In terms of policy formulation and implementation, emphasis is placed on synergy, ensuring the effectiveness and sustainability of governance policies through cross departmental collaboration, policy coordination, public participation, and monitoring feedback mechanisms. The research results indicate that the root mean square error of the proposed method for predicting urban traffic congestion is below 0.1, the average congestion index is always below 2.0, and the average travel time is within 10 minutes, which can significantly improve the urban traffic situation.