Short-term traffic flow prediction model based on IFA optimized RBF neural network

Abstract

Abstract: Aiming at the problem of low accuracy of prediction results caused by the extremely strong uncertainty of short-term traffic flow data, a short-term traffic flow prediction model(IFA-RBF) based on the improved firefly algorithm (IFA) optimized RBF neural network is proposed. By introducing linear decreasing inertial weight and chaos mechanism, the model improves the shortcoming of being trapped into local extremes and lack of population diversity in the later stage of FA. IFA is used to optimize the connection weight and basis function width of the RBF neural network is used to improve the prediction accuracy of the RBF network. Experimental results show that compared with Elman, BP, RBF and FA-RBF models, the constructed model has higher prediction accuracy, and the predicted value has higher fitting degree with the real value.

Key words: intelligent transportation system, traffic flow, short-term prediction, RBF neural network, improved firefly algorithm, chaotic search

CLC Number:

TP18

CAO Jie, ZHANG Min, ZHANG Hong, CHEN Zuo-han, HOU Liang. Short-term traffic flow prediction model based on IFA optimized RBF neural network[J]. Journal of Lanzhou University of Technology, 2022, 48(4): 99-104.

References

[1] 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.
[2] VLAHOGIANNI E L,KARLAFTIS M G,GOLIAS J C.Short-term traffic forecasting: where we are and where we’re going [J].Transportation Research Part C,2014,43:3-19.
[3] ZHANG H,WANG X,CAO J,et al.A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics [J].Applied Intelligence,2018,48(8):2429-2440.
[4] YOUNES M B,BOUKERCHE A.A performance evaluation of an efficient traffic congestion detection protocol (ecode) for intelligent transportation systems [J].AdHoc Netw,2015,24 (2):317-336.
[5] 黄益绍,韩磊.基于改进极限学习机的公交站点短时客流预测方法 [J].交通运输系统工程与信息,2019,19(4):115-123.
[6] 傅蔚阳,刘以安,薛松.基于灰狼算法与小波神经网络的目标威胁评估 [J].浙江大学学报(工学版),2018,52(4):680-686.
[7] 张文胜,郝孜奇,朱冀军,等.基于改进灰狼算法优化BP神经网络的短时交通流预测模型 [J].交通运输系统工程与信息,2020,20(2):196-203.
[8] 罗文慧,董宝田,王泽胜.基于 CNN-SVR 混合深度学习模型的短时交通流预测 [J].交通运输系统工程与信息,2017,17(5):68-74.
[9] CHEN X,CAI X,LIANG J,et al.Ensemble learning multiple LSSVR with improved harmony search algorithm for short-term traffic flow forecasting [J].IEEE Access,2018,6:1-10.
[10] AIT-EI-FQUIH B,HOTEIT I.Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models [J].IEEE Transactions on Signal Processing,2015,63(21):5853-5867.
[11] CHEN D W.Research on traffic flow prediction in the big data environment based on the improved RBF neural network [J].IEEE Transactions on Industrial Informatics,2017,13:2000-2008.
[12] WEI D F.Network traffic prediction based on RBF neural network optimized by improved gravitation search algorithm [J].Neural Computing and Applications,2017,28:2303-2312.
[13] WANG P,WU C,GAO X.Research on subway passenger flow combination prediction model based on RBF neural networks and LSSVM [C]//2016 Chinese Control and Decision Conference (CCDC).[S.l.]:IEEE,2016:6064-6069.
[14] 陈明猜,於东军,戚湧.基于FOA-RBF网络的城市道路短时交通流预测 [J].南京邮电大学学报(自然科学版),2018,38(2):103-110.
[15] TILAHUN S L,NGNOTCHOUYE J M T.Firefly algorithm for discrete optimization problems:a survey [J].Ksce Journal of Civil Engineering,2017,21(2):535-545.
[16] WANG B,LI D X,JIANG J P,et al.A modified firefly algorithm based on light intensity difference [J].Journal of Combinatorial Optimization,2016,31(3):1045-1060.
[17] 宋志强,耿聃,苏晨辉,等.基于改进萤火虫算法优化BP神经网络的水电站厂房振动预测 [J].振动与冲击,2017,36(24):64-69.
[18] 柳长源,任宇艳,毕晓君.基于改进萤火虫算法的区域交通信号配时优化 [J].控制与决策,2020(12):16-21.
[19] 田东平,田絮资.基于混沌映射的PSO及其在图像分割中的应用 [J].西北大学学报(自然科学版),2012,42(6):925-930.
[20] 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.
[21] HOU Y,EDARA P,SUN C.Traffic flow forecasting for urban work zones [J].IEEE Transactions on Intelligent Transportation Systems,2015,16(4):1761-1770.
[22] ZHU J Z,CAO J X,ZHU Y.Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections [J].Transportation Research Part C:Emerging Technologies,2014,47(2): 139-154.
[23] 龙文,蔡绍洪,焦建军,等.求解约束优化问题的萤火虫算法及其工程应用 [J].中南大学学报(自然科学版),2015,46(4):1260-1266.
[24] OLATOMIWA L,KEKHILEF S,SHAMSHIRBAND S,et al.A support vector machine-firefly algorithm-based model for global solar radiation prediction [J].Solar Energy,2015,115(5):632-644.
[25] LIU J,WU N Q,QIAO Y,et al.Short-term traffic flow forecasting using ensemble approach based on deep belief networks [J].IEEE Transactions on Intelligent Transportation Systems,2020,654(99):1-14.