Highway freight volume prediction method and system based on deep learning network

Abstract

The invention discloses a deep learning network-based expressway freight volume prediction method and system. The method comprises the steps of obtaining input data of a model; constructing a highway network diagram, calculating a Dijkstra matrix, calculating a Pearson coefficient matrix of the entrance truck flow and the exit truck flow, and combining the Dijkstra matrix and the Pearson coefficient matrix to form a composite adjacent matrix; inputting the input data and the composite adjacent matrix into a graph convolutional layer of the model, fusing a Laplacian matrix and a spatial attention weight matrix, and aggregating spatial information by adopting a graph convolutional neural network; fusing the input data with the time attention weight matrix, and learning time features by using a long-short term memory network; and carrying out inverse normalization on the output of the connection layer to generate a final prediction result. The method fully considers the space information aggregation capability of the graph convolutional neural network and the time sequence learning capability of the long and short term memory network, can obtain a high prediction result, and can be widely applied to the field of intelligent traffic.

Description

technical field [0001] The invention relates to the field of artificial intelligence-intelligent transportation, in particular to a method and system for predicting expressway freight volume based on a deep learning network. Background technique [0002] With the continuous development of transportation infrastructure construction and continuous improvement of road transportation network, the traffic management and control of expressway system is becoming more and more important. In order to fully understand and control expressway traffic conditions, expressway traffic data is collected accurately in real time through expressway toll stations, ETC gantry, etc.; through information mining of expressway traffic data, expressway traffic volume prediction is carried out, and then analyzed The traffic operation status in the future will provide the basis for the traffic management department to make traffic management and control decisions, ensure traffic safety and reduce traffi...

AI Technical Summary

Problems solved by technology

[0003] Traffic volume forecasting is an important part of traffic data processing and analysis, and it is also an indispensable part of the intelligent highway system. However, the current research on traffic volume forecasting mainly focuses on traffic flow forecasting and travel time forecasting. The study conducts relevant forecasting research on freight volume

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