Research on short-term traffic flow forecasting method based on SARIMA-GA-Elman combined model

Abstract

Abstract: Aiming at the limitations of a single model that can not dig into the complex linear and nonlinear characteristics of traffic flow and the slow convergence of neural network models during training, a combined prediction model based on SARIMA-GA-Elman is proposed. The combined model effectively combines the excellent linear fitting ability of the seasonal autoregressive integrated moving average (SARIMA) model and the powerful nonlinear mapping ability of the Elman recurrent neural network. In the prediction process, the linear component of the time series is firstly predicted based on the SARIMA rolling, and then the prediction error sequence of the SARIMA model is used to establish the Elman-RNN to construct the nonlinear error model. In addition, in the process of training the nonlinear error model, the binary-coded genetic algorithm (GA) is used to optimize the Elman-RNN in order to improve the training efficiency of Elman-RNN. Finally the prediction results of the two models are weighted and combined to obtain the final prediction value. Experimental results show that the combined model has a significant improvement in prediction accuracy and robustness compared to a single model.

Key words: intelligent transportation, traffic flow forecast, combined model, time series, SARIMA, Elman-RNN

CLC Number:

U491.14

ZHANG Xi-jun, WANG Chen-hui. Research on short-term traffic flow forecasting method based on SARIMA-GA-Elman combined model[J]. Journal of Lanzhou University of Technology, 2022, 48(5): 107-113.

References

[1] MCCAULEY J W,PATEL P,CHEN M,et al.xAlON:A brief history of its emergence and evolution [J].Journal of the European Ceramic Society,2009,29(2):223-236.
[2] TAN H,WU Y,SHEN B,et al.Short-term traffic prediction based on dynamic tensor completion [J].IEEE Transactions on Intelligent Transportation Systems,2016,17(8):2123-2133.
[3] WAN J X,LI J,ZHANG S K.Prediction model for ship traffic flow considering periodic fluctuation factors [C]//Electronic and Automation Control Conference (IAEAC).Sanya:IEEE,2018:1506-1510.
[4] KECHAGIAS E P,GAYIALIS S P,KONSTANTAKOPOULOS G D,et al.Traffic flow forecasting for city logistics:A literature review and evaluation [J].International Journal of Decision Support Systems,2019,4(2):159-176.
[5] CHEN D,HU M,MA Y,et al.A network-based dynamic air traffic flow model for short-term en route traffic prediction [J].Journal of Advanced Transportation,2016,50(8):2174-2192.
[6] XIAO J H,XIAO Z,WANG D,et al.Short-term traffic volume prediction by ensemble learning in concept drifting environments [J].Knowledge-Based Systems,2019,164:213-225.
[7] JIA Y,WU J,BEN-AKIVA M,et al.Rainfall-integrated traffic speed prediction using deep learning method [J].IET Intelligent Transport Systems,2017,11(9):531-536.
[8] AQIB M,MEHMOOD R,ALZAHRANI A,et al.A deep learning model to predict vehicles occupancy on freeways for traffic management [J].International Journal of Computer Science and Network Security,2018,18:246-254.
[9] LIN L,LI Y,SADEK A.A k nearest neighbor based local linear wavelet neural network model for on-line short-term traffic volume prediction [J].Procedia-Social and Behavioral Sciences,2013,96:2066-2077.
[10] LIU Y,ZHANG K.Short-term traffic flow forecasting based on wavelet denoising and GA-Elman neural network [J].Technology & Economy in Areas of Communications,2015,17:80-85.
[11] YANG X F,ZHANG L W,XIE W.Forecasting model for urban traffic flow with BP neural network based on genetic Algorithm [C]//Chinese Control and Decision Conference (CCDC).Nanchang:IEEE,2019:4395-4399.
[12] HUANG D R,DENG Z P,ZHAO L,et al.A short-term traffic flow forecasting method based on Markov Chain and Grey Verhulst model [C]//Data Driven Control and Learning Systems (DDCLS).Chongqing:IEEE,2017:606-610.
[13] LI K L,ZHAI C J,XU J M.Short-term traffic flow prediction using a methodology based on ARIMA and RBF-ANN[C]//Chinese Automation Congress (CAC).Jinan:IEEE,2017:2804-2807.
[14] GAO H,ZHAO J Y,JIA L.Short-term traffic flow forecasting model of Elman neural network based on dissimilation particle swarm optimization [C]//International Conference on Networking,Sensing and Control.Sanya:IEEE,2008:1305-1309.
[15] SUN Y S,WANG L Z,WANG X H,et al.Reliability growth prediction method based on GA-Elman neural network [C]//International Conference on Reliability,Maintainability,and Safety (ICRMS).Shanghai:IEEE,2018:135-141.
[16] 张秋余,朱学明.基于GA-Elman神经网络的交通流短时预测方法 [J].兰州理工大学学报,2013,39(3):94-98.
[17] 谭满春,冯荦斌,徐建闽.基于ARIMA与人工神经网络组合模型的交通流预测 [J].中国公路学报,2007,20(4):118-121.