Preliminary communication

https://doi.org/10.17559/TV-20180402112949

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

Yong-sheng Qian ; Lanzhou Jiaotong University, No. 88, West Road, Anning District, Lanzhou City, Gansu, China
Jun-wei Zeng ; Lanzhou Jiaotong University, No. 88, West Road, Anning District, Lanzhou City, Gansu, China
Shan-fu Zhang ; Lanzhou Jiaotong University, No. 88, West Road, Anning District, Lanzhou City, Gansu, China
De-jie Xu ; Lanzhou Jiaotong University, No. 88, West Road, Anning District, Lanzhou City, Gansu, China
Xu-ting Wei ; Lanzhou Jiaotong University, No. 88, West Road, Anning District, Lanzhou City, Gansu, China

Abstract

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.

Keywords

genetic algorithm; highway transportation; phase space reconstruction; RBF neural network; short-term traffic flow prediction; wavelet neural network

Hrčak ID:

242331

URI

https://hrcak.srce.hr/242331

Publication date:

15.8.2020.

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