A Denoising Scheme-Based Traffic Flow Prediction Model: Combination of Ensemble Empirical Mode Decomposition and Fuzzy C-Means Neural Network

In the Intelligent Transportation Systems (ITS), highly accurate traffic flow prediction is considered as key technology to evaluate traffic state of the urban road network. However, due to disturbing from environment, the original traffic flow data may be influenced by noise and finally cause the decline of prediction accuracy. This study design a hybrid prediction model combining Ensemble Empirical Mode Decomposition (EEMD) denoising schemes and classifying learning algorithm based on Fuzzy C-means Neural Network (FCMNN) to improve prediction accuracy. In the model training process, several key parameters in EEMD and FCMNN are determined according to prediction errors based on traffic volume detected from highway network in the Minneapolis city. In the model validation, three widely used indicators for error evaluation are applied to estimate the prediction accuracy of four candidate models under single and multi step, including Artificial Neural Network (ANN), EEMD+ANN, FCMNN and EEMD+FCMNN. The results shown in the case study indicate that the prediction models combined with denoising methods are superior to the models without adopting denoising algorithm. Furthermore, the model using classifying learning method FCMNN can produce higher prediction accuracy than traditional ANN model. In addition, the long-term prediction performance of FCMNN is also much better than that of ANN because that sub-NN system is trained according to each classifying patterns to obtain better optimization effect. Results summarized in this study could be helpful for administration to design managing and controlling strategies according to high prediction accuracy.

[1]  Xiaolei Li,et al.  Traffic Flow Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm , 2016 .

[2]  Mohammad Hossein Fazel Zarandi,et al.  A type-2 fuzzy c-regression clustering algorithm for Takagi-Sugeno system identification and its application in the steel industry , 2012, Inf. Sci..

[3]  Brigitte Chebel-Morello,et al.  Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .

[4]  Yunpeng Wang,et al.  Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .

[5]  Fang Liu,et al.  A hierarchical prediction model for lane-changes based on combination of fuzzy C-means and adaptive neural network , 2019, Expert Syst. Appl..

[6]  Wei Liang,et al.  Short-term travel flow prediction method based on FCM-clustering and ELM , 2017 .

[7]  I. M. S. Lamba,et al.  Kernelized type-2 fuzzy c-means clustering algorithm in segmentation of noisy medical images , 2011, 2011 IEEE Recent Advances in Intelligent Computational Systems.

[8]  Bin Zhou,et al.  Dynamic Analysis of Kalman Filter for Traffic Flow Forecasting in Sensornets , 2012 .

[9]  Zhaohua Wu,et al.  Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[10]  Chiung-Wen Chang,et al.  A functional data approach to missing value imputation and outlier detection for traffic flow data , 2013 .

[11]  Bin Yu,et al.  Missing Data Processing Based on Deep Neural Network Enhanced by K-Means , 2019, ICMLC '19.

[12]  Francisco Javier Díaz Pernas,et al.  Wavelet‐Based Denoising for Traffic Volume Time Series Forecasting with Self‐Organizing Neural Networks , 2010, Comput. Aided Civ. Infrastructure Eng..

[13]  Stephen Dunne,et al.  Weather Adaptive Traffic Prediction Using Neurowavelet Models , 2013, IEEE Transactions on Intelligent Transportation Systems.

[14]  Muhammad Tayyab Asif,et al.  Matrix and Tensor Based Methods for Missing Data Estimation in Large Traffic Networks , 2016, IEEE Transactions on Intelligent Transportation Systems.

[15]  Keechoo Choi,et al.  Denoising traffic collision data using ensemble empirical mode decomposition (EEMD) and its application for constructing continuous risk profile (CRP). , 2014, Accident; analysis and prevention.

[16]  G. Alarcón,et al.  Power spectrum and intracranial EEG patterns at seizure onset in partial epilepsy. , 1995, Electroencephalography and clinical neurophysiology.

[17]  Cyrus Shahabi,et al.  Inferring Traffic Incident Start Time with Loop Sensor Data , 2016, CIKM.

[18]  Guangdong Feng,et al.  A Tensor Based Method for Missing Traffic Data Completion , 2013 .

[19]  I Okutani,et al.  Dynamic prediction of traffic volume through Kalman Filtering , 1984 .

[20]  Li Li,et al.  Efficient missing data imputing for traffic flow by considering temporal and spatial dependence , 2013 .

[21]  Ahsan Kareem,et al.  Time-Frequency Analysis of Nonstationary Process Based on Multivariate Empirical Mode Decomposition , 2016 .

[22]  Mu-Chen Chen,et al.  Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks , 2012 .

[23]  Wei Guan,et al.  Analysis and Prediction of Regional Mobility Patterns of Bus Travellers Using Smart Card Data and Points of Interest Data , 2019, IEEE Transactions on Intelligent Transportation Systems.

[24]  Yinhai Wang,et al.  A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation , 2015 .

[25]  Xinxin Feng,et al.  A Multi-source Based Coupled Tensors Completion Algorithm for Incomplete Traffic Data Imputation , 2019, 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP).

[26]  Bin Ran,et al.  Fuzzy-Neural Network Traffic Prediction Framework with Wavelet Decomposition , 2003 .

[27]  C. Zhou,et al.  An improved methodology for application of wavelet transform to partial discharge measurement denoising , 2005, IEEE Transactions on Dielectrics and Electrical Insulation.

[28]  Mohamed Elshenawy,et al.  Automatic Imputation of Missing Highway Traffic Volume Data , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[29]  Hengchao Li,et al.  A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction , 2015 .

[30]  Ali Selamat,et al.  Improved sensitivity based linear learning method for permeability prediction of carbonate reservoir using interval type-2 fuzzy logic system , 2014, Appl. Soft Comput..

[31]  Fang Liu,et al.  An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic , 2017, IEEE Transactions on Intelligent Transportation Systems.

[32]  Wu Xian-gu.  An Improved Envelope Fitting Algorithm for the Empirical Mode Decomposition , 2015 .

[33]  Danyang Li,et al.  Traffic Flow Prediction during the Holidays Based on DFT and SVR , 2019, J. Sensors.

[34]  Xianglong Luo,et al.  An Algorithm for Traffic Flow Prediction Based on Improved SARIMA and GA , 2018, KSCE Journal of Civil Engineering.

[35]  Lin Shi,et al.  Short-Term Traffic Flow Forecasting Using ARIMA-SVM Algorithm and R , 2018, 2018 5th International Conference on Information Science and Control Engineering (ICISCE).

[36]  Zhirui Ye,et al.  Short‐Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition , 2007, Comput. Aided Civ. Infrastructure Eng..

[37]  W. Y. Szeto,et al.  Short-Term Traffic Speed Forecasting Based on Data Recorded at Irregular Intervals , 2010, IEEE Transactions on Intelligent Transportation Systems.

[38]  Salim Lahmiri,et al.  Image denoising in bidimensional empirical mode decomposition domain: the role of Student's probability distribution function. , 2016, Healthcare technology letters.

[39]  Yajie Zou,et al.  A space–time diurnal method for short-term freeway travel time prediction , 2014 .

[40]  D Gordon E Robertson,et al.  Design and responses of Butterworth and critically damped digital filters. , 2003, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[41]  Byoung-Jo Yoon,et al.  Potentialities of Data-Driven Nonparametric Regression in Urban Signalized Traffic Flow Forecasting , 2014 .

[42]  Jinjun Tang,et al.  Traffic flow prediction based on combination of support vector machine and data denoising schemes , 2019, Physica A: Statistical Mechanics and its Applications.

[43]  Huan Wang,et al.  A Short-term Traffic Flow Forecasting Method Based on the Hybrid PSO-SVR , 2015, Neural Processing Letters.

[44]  Haitao Li,et al.  Research on prediction of traffic flow based on dynamic fuzzy neural networks , 2015, Neural Computing and Applications.

[45]  Zili Zhang,et al.  A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting , 2016, Neurocomputing.

[46]  Fei-Yue Wang,et al.  An efficient realization of deep learning for traffic data imputation , 2016 .

[47]  Hong Gu,et al.  A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data , 2010, Expert Syst. Appl..

[48]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.