Traffic Flow Prediction Based on Hybrid Model of Auto-Regressive Integrated Moving Average and Genetic Particle Swarm Optimization Wavelet Neural Network

Lisheng YIN; Shengqi TANG; Sheng LI; Yigang HE

doi:10.11999/JEIT181073

Volume 41 Issue 9

Sep. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(9): 2273-2279

Xu Shao-Kun, Liu Ji-Hong, Yuan Xiang-Yu, Lu Jing. Two Dimensional Geometric Feature Inversion Method for Midcourse Target Based on ISAR Image[J]. Journal of Electronics & Information Technology, 2015, 37(2): 339-345. doi: 10.11999/JEIT140338

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Lisheng YIN, Shengqi TANG, Sheng LI, Yigang HE. Traffic Flow Prediction Based on Hybrid Model of Auto-Regressive Integrated Moving Average and Genetic Particle Swarm Optimization Wavelet Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2273-2279. doi: 10.11999/JEIT181073

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Traffic Flow Prediction Based on Hybrid Model of Auto-Regressive Integrated Moving Average and Genetic Particle Swarm Optimization Wavelet Neural Network

doi: 10.11999/JEIT181073

School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China

Funds: The National Natural Science Foundation of China (51577046, 61673153), The National Defense Advanced Research Project (C1120110004, 9140A27020211DZ5102), The Key Grant Project of Chinese Ministry of Education (313018), Anhui Provincial Science and Technology Foundation of China (1301022036)

Received Date: 2018-11-22
Rev Recd Date: 2019-03-29

Available Online: 2019-04-03

Publish Date: 2019-09-10

Abstract

Abstract

In view of the nonlinear and stochastic characteristics of short-term traffic flow data, this article proposes a prediction model and algorithm based on hybrid Auto-Regressive Integrated Moving Average (ARIMA) and Genetic Particle Swarm Optimization Wavelet Neural Network (GPSOWNN) in order to improve its prediction accuracy and rate of convergence. In terms of model construction, the ARIMA model prediction value and the historical data of the first three moments with strong correlation with gray correlation coefficient greater than 0.6 are used as input of the Wavelet Neural Network(WNN), and the structure of the model is simplified considering both the stationary and non-stationary historical data. In terms of algorithm, by using the genetic particle swarm optimization algorithm to select optimally the initial values of the wavelet neural network, the results can speed up the convergence of network training under the condition that it is not easy to fall into local optimum. The experimental results show that the proposed model is superior to hybrid ARIMA and GPSOWNN in terms of prediction accuracy, the genetic particle swarm optimization algorithm is superior to the genetic algorithm optimization model in terms of convergence speed.

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References(14)

References

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