1. College of Transportation & Logistics, Southwest Jiaotong University, Sichuan Chengdu 610031, China;
2. College of Information Science & Technology, Chengdu University of Technology, Sichuan Chengdu 610031, China
Short-term Traffic Flow Prediction Based on the IMM-BP-UKF Model
ZHOU Xing-yu1, LI Hong-mei1, ZHENG Wei-Hao1, TANG Zhi-hui1, YANG Li-jun2
1. College of Transportation & Logistics, Southwest Jiaotong University, Sichuan Chengdu 610031, China;
2. College of Information Science & Technology, Chengdu University of Technology, Sichuan Chengdu 610031, China
摘要This study aims to obtain satisfactory short-term traffic flow forecasting results in various traffic flow modes. Clustering analysis of traffic flow data is carried out using the Kohonen neural network. Then, different BP neural networks are trained using different classified traffic flow data. Different neural network models are combined with UKF to form multiple traffic estimators to realize the estimation function. Finally, the interactive method is used to fuse the prediction results of each estimator, and comprehensive traffic flow forecasting results are obtained. With the simulation example, a number of single estimators are obtained, and a joint estimator based on this method is constructed. The section flow of one given road is predicted to verify the performance of the estimator. Results show that the joint estimator has a higher prediction accuracy than the single estimator and has adaptive characteristics when traffic flow characteristics change.
Abstract：This study aims to obtain satisfactory short-term traffic flow forecasting results in various traffic flow modes. Clustering analysis of traffic flow data is carried out using the Kohonen neural network. Then, different BP neural networks are trained using different classified traffic flow data. Different neural network models are combined with UKF to form multiple traffic estimators to realize the estimation function. Finally, the interactive method is used to fuse the prediction results of each estimator, and comprehensive traffic flow forecasting results are obtained. With the simulation example, a number of single estimators are obtained, and a joint estimator based on this method is constructed. The section flow of one given road is predicted to verify the performance of the estimator. Results show that the joint estimator has a higher prediction accuracy than the single estimator and has adaptive characteristics when traffic flow characteristics change.
周星宇, 李红梅, 郑伟皓, 唐智慧, 杨丽君. 基于交互式BP-UKF模型的短时交通流预测方法[J]. Journal of Highway and Transportation Research and Development, 2019, 13(2): 56-64.
ZHOU Xing-yu, LI Hong-mei, ZHENG Wei-Hao, TANG Zhi-hui, YANG Li-jun. Short-term Traffic Flow Prediction Based on the IMM-BP-UKF Model. Journal of Highway and Transportation Research and Development, 2019, 13(2): 56-64.
[1] YANG Z S. Theory and Model of Urban Traffic Flow Guidance System[M].Beijing:China Communications Press, 2000.(in Chinese)
[2] YANG Z S. Basic Traffic Information Fusion Technology and application[M].Beijing:China Railway Publishing House, 2005. (in Chinese)
[3] MAI T, GHOSH B, WILSON S. Multivariate Short-Term Traffic Flow Forecasting Using Bayesian Vector Autoregressive Moving Average Model[C]//.Transportation Research Board 91st Annual Meeting. Dublin:TBB, 2012:3728-3740.
[4] XU Dong-wei, WANG Yong-dong,JIA Li-min, et al. Real-time Road Traffic State Prediction Based on ARIMA and Kalman Filter[J]. Frontiers of Information Technology & Electronic Engineering,2017,18(2):287-303.
[5] WANG Jin, SHI Qi-xin.Short-term Traffic Speed Forecasting Hybrid Model Based on Chaos-Wavelet Analysis-Support Vector Machine theory[J]. Transportation Research Part C.2012,(27):219-232.
[6] WANG Y, PAPAGEORGIOU M, MESSMER A. Real-time Freeway Traffic State Estimation Based on Extended Kalman Filter:Adaptive Capabilities and Real Data Testing[J]. 2008, 42(10):1340-1358.
[7] LIU Zhao,DU Wei,YAN Dong-mei,et al. Short-term Traffic Flow Forecast Based on Combination of K Nearest Neighbor Algorithm and Support Vector Regression[J]. Journal of Highway and Transportation Research and Development, 2017,34(5):122-128+158. (in Chinese)
[8] JIANG Shi -zheng,XU Rong,CHEN Qin-mei. Complex Network Short-Term Traffic Forecasting Based on Lasso-NN Model[J]. Journal of Shanghai Jiaotong University,2015,49(2):281-286. (in Chinese)
[9] WANG Jiao, Li Jun.Application of Minimax Probability Machine Regression in Short-term Traffic Flow Prediction[J]. Journal of Highway and Transportation Research and Development,2014,31(02):121-127(in Chinese)
[10] FAN Na,ZHAO Xiang-mo,DAI Min,An Yi-Shen. Short Term Traffic Flow Prediction Model[J]. Journal of Traffic and Transportation Engineering,2012,12(4):114-119. (in Chinese)
[11] YANG Chun-xia,FU Yi-qin,BAO Tie-nan. Short-term Traffic Flow Prediction Based on Similarity[J]. Journal of Highway and Transportation Research and Development,2015,32(04):114-119. (in Chinese)
[12] LI X R, JILKOV V P, RU J. Multiple-model Estimation with Variable Structure-part VI:Expected-mode Augmentation[J]. IEEE Transactions on Aerospace &Electronic Systems, 2005, 41(3):853-867.
[13] MA Shu-qin. Research on Network Intrusion Clustering Based on Kohonen Neural Network Algorithm[J]. China Measurement & Test,2013,39(4):113-116. (in Chinese)
[14] LIU Hun,CHEN Xiao-hui,LIU Yin-feng. Multi-sensor Information Fusion Based on Rough Set and Fuzzy Kohonen Clus-tering Network[J], Journal of Electronic Measurement and Instrumentation,2010,24(03):218-223. (in Chinese)
[15] JULIER S,UHLMANN J,DURRANT-WHYTE HF.A New Method for the Nonlinear Transformation of Means and Covariances in Filters and Estimators[J]IEEE Transactions on Automatic Control.2000,45(3):477-482.
[16] WEI H K,XU S X,SONG W Z. Generalization Theory and Generalization Methods for Neural Networks[J]. Acta Automatica Sinica,2001,27(6):806-815.(in Chinese)
[17] TANG Zhi-hui,MOU Rui-fang,ZUO Yan-liang. The Technology of Transportation Safety[M],Chengdu:Southwest Jiaotong University Press,2018. (in Chinese)
[18] CUI Jin-tai,DAI Hong-de. Kalman Filtering with Real-Time Applications[M],Beijing:Tsinghua University Press,2013.(in Chinese)
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2019, Vol. 13 
