Accurate traffic flow prediction is crucial for making informed decisions regarding travel route selection and mitigating traffic congestion. This paper introduces a WGCG model that addresses the impact of traffic flow noise by combining wavelet transform with GCN and GRU models. The Sym6 wavelet basis is utilized to decompose traffic flow into two layers, effectively reducing noise. The road network's topological structure features are described using an undirected graph and adjacency matrix, with GCN extracting spatial rules and GRU mining hidden time correlation information from historical traffic flow data. The WGCG model integrates these modules to capture the dynamic patterns of traffic flow comprehensively. The model's performance is evaluated on a real dataset from Beijing's Second Ring Road, comparing prediction results with baseline models like HA, SVR, GRU, and GCN. Experimental findings indicate that the WGCG model achieves a significant increase in prediction accuracy, reducing errors by 27.9%, 22.3%, and 21.7% respectively, compared to the second-best model SVR. Ablation experiments further demonstrate that the WGCG model outperforms combined models utilizing only specific modules, confirming its feasibility and superiority.
Accurate and efficient prediction of road travel time plays a significant role in the application of intelligent transportation system. In order to accurately predict travel time, a new attention-based CNN-BiGRU hybrid model is proposed, which can simultaneously capture the spatial-temporal features of travel time. In this model, convolutional neural network (CNN) and bi-directional gated recurrent unit (BiGRU) are used to collect the spatial and temporal characteristics of travel time separately. The attention mechanism was used for assigning different weights according to the importance of the data to further improve the prediction accuracy of the model. The model is verified by using the charging data of Guangzhou airport south line, and the experiment shows that the model can achieve accurate travel time prediction.
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