Study On Intelligent Transportation Management Mode Based On Deep Learning

Intelligence analysis is driven by goals.The process of acquiring information and knowledge through intelligence technology is an important part of intelligence work.With the development of the Internet,data resources continue to be enriched,and traditional intelligence analysis methods can no longer fully tap and utilize the potential value of multi-source,multi-dimensional,and multi-type data.With the development of artificial intelligence,intelligence analysis methods based on deep learning and big data are becoming more and more extensive.Deep learning provides full process technical support for information service work in information collection,storage,analysis and even decision-making.This article takes traffic flow prediction as an example,and illustrates the important role played by deep learning technology in intelligence analysis through traffic flow prediction.In order to alleviate the current situation of urban traffic congestion and establish an efficient,scientific and accurate traffic flow prediction system,this paper proposes the LSTM-GCN traffic flow prediction model,which combines long-short term memory network(Long Short term memory,LSTM)and graph convolutional neural network(Graph Convolutional Network,GCN)to obtain traffic Flow spatio-temporal feature information,of which the LSTM is used to obtain the time-series features of historical traffic flow data,and the obtained features are used as the node features of the GCN,and the features of the non-Euclidean graph data of the road network structure are used as the features of the edges of the graph convolutional neural network.The two are combined with the input graph convolutional neural network to predict the traffic flow;then,the original data of the traffic flow and the original data of the graph structure are preprocessed.The processed unstructured data is input into the model for learning,training and prediction.During the training process,the parameters are continuously adjusted until the optimal structure of the prediction model is reached.The availability of the proposed traffic flow prediction model is verified through experiments.The results show that the accuracy of the prediction model reaches 88.53%,and the LSTM-GCN prediction model proposed in this paper has higher prediction accuracy than the Linear Support Vector Regression(LSVR)and K-Nearest Neighbor(KNN)models;finally,the new traffic management model based on artificial intelligence and big data technology is explored,the application mechanism and actual application scenarios of the traffic flow prediction model are proposed,and summarizes the changes of the new traffic management model and lays the foundation for the practical application of traffic flow prediction models.