Multi-perspective Attention Gated Unit Network:Method Optimization And Parking Space Prediction Application

With the continuous development of social economy,automobiles have entered thousands of households,and the number of automobiles has been rapidly increased.The popularity of automobiles shows that social and economic changes have enriched people’s lives,but the resulting social problems should not be underestimated.The rapid growth of car ownership has caused a serious imbalance in the ratio of cars and parking spaces in major cities in my country.At the same time,due to the asymmetry of parking space information on the supply side and the demand side,the vacancy rate of many parking lots is as high as 50%.Seemingly contradictory phenomena have caused widespread traffic congestion problems in major cities,and parking problems are plaguing every driver,bringing enormous pressure to road resource management.The prediction information of the number of parking spaces is of great significance for improving the utilization rate of parking spaces and alleviating traffic congestion.It can balance the distribution of the number of parking spaces occupied by the parking lot.However,most of the current parking space prediction algorithms only focus on the temporal correlation of data,and there are relatively few studies on spatial correlation.In order to deeply explore the important role of spatial information in the prediction of the number of parking spaces and further improve the accuracy of the parking space prediction algorithm,this paper proposes a Multi-View Graph Attention Gating Unit Network(MGA-GRU)Model,which transforms the Gate Recurrent Unit(GRU)neural network,and adds a graph attention convolution gate unit(GA)for extracting spatial information.The graph attention convolution gating unit enables the MGA-GRU model to simultaneously extract and process temporal features and spatial features,reducing the mutual interference between spatio-temporal information.The attention mechanism adopted can be based on the attention between different node sequences.It is more scientific and reasonable to aggregate the information of neighbor nodes by using the force coefficient.In addition,when the MGA-GRU model constructs the relationship graph,it integrates a variety of spatial information to construct a multi-view topological graph,which makes the spatial features more comprehensive and accurate,and further improves the prediction effect of the model.In order to verify the effect of the model,we conducted experiments on the real data set Lanzhou parking lot data set,which proved that the MGA-GRU model is superior to most of the existing models in both short-term and long-term parking space prediction,and the prediction results are in many reached the optimum in terms of indicators.Finally,this paper also conducts a practical exploration.In order to promote the model and obtain wider use,this paper develops a parking space prediction application based on the MGA-GRU model to tap the practical value of the model.