Expressway traffic flow prediction method based on DenseNet

Abstract

The invention discloses an expressway traffic flow prediction method based on a DenseNet. On the basis of researching a deep two-dimensional convolution network DenseNet, one-dimensional traffic flowdata is imported, in addition, input and convolution ways in a network are revised, so that the network learns a hidden rule in one-dimensional time sequence data, and expressway traffic flow prediction of a next stage is effectively realized. The method comprises the following implementation steps that: (1) reading traffic flow data, and constructing a training test set; (2) carrying out data preprocessing, transforming a four-dimensional tensor, and carrying out normalization to adapt to network learning; (3) constructing the network; (4) training the network, inputting a training sample, updating network parameters through a forward direction prediction result and error backpropagation, and carrying out loop iteration until network convergence is realized; and (5) testing the network, and carrying out traffic flow prediction on the test set. The method can automatically learn a hidden characteristic relationship between flows in a flow dataset, has a better prediction effect and iswidely applied to traffic flow prediction.

Description

technical field [0001] The present invention relates to a highway traffic flow prediction system and method, in particular to a highway traffic flow prediction system and method based on DenseNet. Background technique [0002] Based on the traffic flow monitoring data of real-time road conditions, the prediction of expressway traffic flow plays an important role in promoting the process of traffic informatization. Its purpose is to accurately collect traffic flow data in real time through video cameras, radar and other sensing terminals; to intervene in advance according to the actual traffic operation conditions, weaken the influence of various factors on traffic flow, and prevent traffic accidents caused by vehicle failures and traffic accidents. Expressway congestion; ensure smooth roads and driving safety, and realize the harmonious operation of people, vehicles and roads. [0003] According to reliable estimates, by 2020, the national expressway network will be basical...

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the shortcomings of the prior art, the present invention provides a DenseNet-based expressway traffic flow prediction method, which overcomes the shortcomings of some traditional networks that cannot consider the potential trend of traffic flow data, and achieves the effect of higher model accuracy

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