Short-Term Traffic Prediction Based on Genetic Algorithm Improved Neural Network

被引：19
|
作者：
Qian, Yong-sheng [1 ]
Zeng, Jun-wei [1 ]
Zhang, Shan-fu [1 ]
Xu, De-jie [1 ]
Wei, Xu-ting [1 ]
机构：
[1] Lanzhou Jiaotong Univ, 88 West Rd, Lanzhou, Gansu, Peoples R China
来源：
TEHNICKI VJESNIK-TECHNICAL GAZETTE | 2020年 / 27卷 / 04期
基金：
中国国家自然科学基金;
关键词：
genetic algorithm; highway transportation; phase space reconstruction; RBF neural network; short-term traffic flow prediction; wavelet neural network; KALMAN FILTER; FLOW; DYNAMICS;
D O I：
10.17559/TV-20180402112949
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This paper takes the time series of short-term traffic flow as research object. The delay time and embedding dimension are calculated by C-C algorithm, and the chaotic characteristics of the time series are verified by small data sets method.Then based on the neural network prediction model and the chaotic phase space reconstruction theory, the network topology is determined, and the prediction is conducted by the wavelet neural network and RBF neural network using Lan-Hai expressway experimental data. The results show that the prediction effect of RBF neural network is better. Due to the poor stability of the network caused by the initial parameters randomness, the genetic algorithm is used to optimize the initial parameters. The results show that the prediction error of the optimized wavelet neural network or RBF neural network is reduced by more than 10%, and prediction accuracy of the latter is better.
引用
收藏
页码：1270 / 1276
页数：7
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