Expressway Traffic Volume Prediction Method Based on Improved GCN-sbuLSTM Model
In order to address the shortcomings of existing expressway traffic volume prediction methods in capturing dynamic spatio-temporal dependencies,the paper proposed a novel traffic vol-ume prediction model for expressway networks that incorporates information geometry methods and attention mechanisms.Initially,an information geometry method was used to quantify the difference in dynamic data distribution between ETC gantries.Subsequently,an attention-based mechanism was devised to encapsulate the dynamic spatial dependencies inherent in traffic patterns.In conclusion,by amalgamating a configuration of stacked bidirectional recurrent layers,a long-time span parallel sub-model algorithm named IGAGCN-sbuLSTM was proposed.This acronym denoted a synergistic mod-el that combined GCN(Graph Convolutional Neural Network),which was optimized using Informa-tion Geometry Approach and Attention Mechanism,with sbuLSTM(Stacked Bidirectional Unidirec-tional Long Short-Term Memory Neural Network).Leveraging over 100 road segments,surpassing 3 000 gantries from nearly 700 billion entries in the expressway ETC gantry system database,experi-mental outcomes demonstrated that the IGAGCN-sbuLSTM algorithm,when benchmarked against ex-isting models such as LSTM,GCN,GCN-LSTM,ASTGCN,registered a reduction in MAE(Mean Absolute Error)by 2.39,3.72,1.02,and 1.46 respectively on a 10-minute time scale.The RMSE(Root Mean Square Error)diminished by 3.25,4.32,2.05,and 5.65,while the MAPE(Mean Absolute Percentage Error)decreased by 5.49%,12.54%,1.56%,and 0.5%respectively.These results affirm that the IGAGCN-sbuLSTM model surpasses its predecessors with single capture characteristic and other prevalent combined models in terms of predictive accuracy and capability to handle expanding time intervals,rendering it highly applicable for predictions analysis in areas such as expressway toll-ing and vehicular speeds.
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