Predicting the Time Required to Pass Congested Road Based on Neural Network Algorithm


Predicting the Time Required to Pass Congested Road Based on Neural Network Algorithm


Zhong Zheng1*, Yu Cao2, Hairui Zhang2, Tianlong Wang3, Yunxiao Wu1

1College of Electrical Engineering & New Energy, China Three Gorges University, Yichang, 443002,China. 2College of Science, China Three Gorges University, Yichang, 443002, China. 3College of Civil Engineering & Architecture, China Three Gorges University, Yichang, 443002, China.


Scientific Research and Reviews

In order to predict the duration of traffic congestion, this paper established a traffic congestion evaluation model based on cumulative ratio Logistic regression and a traffic congestion time prediction model based on BP neural network. Combining Pearson test, numerical combination, standard deviation method and other methods to solve the problem. Based on the measured data of Jinshui Road in Zhengzhou, the average error is 0.019m/ s and the prediction error rate is 0.15%, both within a reasonable range. The model can improve the accuracy of congestion time prediction and provide some help to real life.


Keywords: Cumulative ratio Logistic regression; BP neural network; Pearson test; Standard deviation method

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How to cite this article:
Zhong Zheng, Yu Cao, Hairui Zhang, Tianlong Wang, Yunxiao Wu. Predicting the Time Required to Pass Congested Road Based on Neural Network Algorithm. Scientific Research and Reviews, 2020; 13:112. DOI:10.28933/srr-2020-02-1005


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