Short-term traffic flow prediction based on empirical mode decomposition and long short-term memory neural network
ZHANG Xiaohan1, FENG Aimin1,2
1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 210000, China; 2. College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 210000, China
Abstract：Traffic flow prediction is an important part of intelligent transportation. The traffic data to be processed by it are non-linear, periodic, and random, as a result, the unstable traffic flow data depend on long-term data range during data prediction. At the same time, due to some external factors, the original data often contain some noise, which may further lead to the degradation of prediction performance. Aiming at the above problems, a prediction algorithm named EMD-LSTM that can denoise and process long-term dependence was proposed. Firstly, Empirical Mode Decomposition (EMD) was used to decompose different scale components in the traffic time series data gradually to generate a series of intrinsic mode functions with the same feature scale, thereby removing certain noise influence. Then, with the help of Long Short-Term Memory (LSTM) neural network, the problem of long-term dependence of data was solved, so that the algorithm performed more outstanding in long-term field prediction. Experimental results of short-term prediction of actual datasets show that EMD-LSTM has the Mean Absolute Error (MAE) 1.916 32 lower than LSTM, and the Mean Absolute Percentage Error (MAPE) 4.645 45 percentage points lower than LSTM. It can be seen that the proposed hybrid model significantly improves the prediction accuracy and can solve the problem of traffic data effectively.
[1] ZHOU T,HAN G,XU X,et al. A learning-based multimodel integrated framework for dynamic traffic flow forecasting[J]. Neural Processing Letters,2019,49(1):407-430. [2] ZENG B,LI C. Improved multi-variable grey forecasting model with a dynamic background-value coefficient and its application[J]. Computers and Industrial Engineering,2018,118:278-290. [3] WILLIAMS B M, DURVASULA P K, BROWN D E. Urban freeway traffic flow prediction:application of seasonal autoregressive integrated moving average and exponential smoothing models[J]. Transportation Research Record,1998,1644(1):132-141. [4] XIE Y C,ZHANG Y,YE Z. Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition[J]. Computer-Aided Civil and Infrastructure Engineering,2007,22(5):326-334. [5] GUO J,HUANG W,WILLIAMS B M. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification[J]. Transportation Research Part C:Emerging Technologies,2014,43(Pt 1):50-64. [6] WANG Y,VAN SCHUPPEN J H,VRANCKEN J. Prediction of traffic flow at the boundary of a motorway network[J]. IEEE Transactions on Intelligent Transportation Systems,2014,15(1):214-227. [7] COMERT G,BEZUGLOV A. An online change-point-based model for traffic parameter prediction[J]. IEEE Transactions on Intelligent Transportation Systems,2013,14(3):1360-1369. [8] ZHANG X,PANG Y,CUI M,et al. Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model[J]. Annals of Epidemiology,2015,25(2):101-106. [9] MIN W,WYNTER L. Real-time road traffic prediction with spatiotemporal correlations[J]. Transportation Research Part C:Emerging Technologies,2011,19(4):606-616. [10] MA T,ZHOU Z,ABDULHAI B. Nonlinear multivariate timespace threshold vector error correction model for short term traffic state prediction[J]. Transportation Research Part B:Methodological,2015,76:27-47. [11] TCHRAKIAN T T,BASU B,O'MAHONY M,et al. Real-time traffic flow forecasting using spectral analysis[J]. IEEE Transactions on Intelligent Transportation Systems,2012,13(2):519-526. [12] ZHANG Y,ZHANG Y,HAGHANI A. A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model[J]. Transportation Research Part C:Emerging Technologies,2014,43(Pt 1):65-78. [13] GHOSH B,BASU B,O'MAHONY M. Multivariate short-term traffic flow forecasting using time-series analysis[J]. IEEE Transactions on Intelligent Transportation Systems,2009,10(2):246-254. [14] HU X, XU X, XIAO Y, et al. SINet:a scale-insensitive convolutional neural network for fast vehicle detection[J]. IEEE Transactions on Intelligent Transportation Systems,2019,20(3):1010-1019. [15] CAI P,WANG Y,LU G,et al. A spatiotemporal correlative knearest neighbor model for short-term traffic multistep forecasting[J]. Transportation Research Part C:Emerging Technologies, 2016,62:21-34. [16] ABU ARQUB O,ABO-HAMMOUR Z S. Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm[J]. Information Sciences,2014,279:396-415. [17] ABU ARQUB O,AL-SMADI M,MOMANI S,et al. Application of reproducing kernel algorithm for solving second-order,two-point fuzzy boundary value problems[J]. Soft Computing,2017,21(23):7191-7206. [18] CAI W,YU D,WU Z,et al. A hybrid ensemble learning framework for basketball outcomes prediction[J]. Physica A:Statistical Mechanics and its Applications, 2019, 528:No. 121461. [19] CHENG A,JIANG X,LI Y,et al. Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method[J]. Physica A:Statistical Mechanics and its Applications,2017,466:422-434. [20] XIAO J,WEI C,LIU Y. Speed estimation of traffic flow using multiple kernel support vector regression[J]. Physica A:Statistical Mechanics and its Applications,2018,509:989-997. [21] 刘明宇, 吴建平, 王钰博, 等. 基于深度学习的交通流量预测[J]. 系统仿真学报,2018,30(11):4100-4105,4114.(LIU M Y,WU J P,WANG Y B,et al. Traffic flow prediction based on deep learning[J]. Journal of System Simulation,2018,30(11):4100-4105,4114.) [22] 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.(LUO W H,DONG B T,WANG Z S. Short-term traffic flow prediction based on CNN-SVR hybrid deep learning model[J]. Journal of Transportation Systems Engineering and Information Technology,2017,17(5):68-74.) [23] HUANG N E,SHEN Z,LONG S R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J]. Proceedings of the Royal Society A:Mathematical Physical and Engineering Sciences, 1998,454(1971):903-995. [24] NUNES J C,BOUAOUNE Y,DELECHELLE E,et al. Image analysis by bidimensional empirical mode decomposition[J]. Image and Vision Computing,2003,21(12):1019-1026. [25] 叶林, 刘鹏. 基于经验模态分解和支持向量机的短期风电功率组合预测模型[J]. 中国电机工程学报,2011,31(31):102-108.(YE L,LIU P. Combined model based on EMD-SVM for short-term wind power prediction[J]. Proceedings of the CSEE, 2011,31(31):102-108.) [26] LIU B,RIEMENSCHNEIDER S,XU Y. Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum[J]. Mechanical Systems and Signal Processing, 2006, 20(3):718-734. [27] YILDIRIM S,JOTHIMANI D,KAVAKLIOĞLU C,et al. Deep learning approaches for sentiment analysis on financial microblog dataset[C]//Proceeding of the 2019 IEEE International Conference on Big Data. Piscataway:IEEE,2019:5581-5584.
2021, Vol. 41 
