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

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 11546-11559 ◽  
Author(s):  
Jinjun Tang ◽  
Fan Gao ◽  
Fang Liu ◽  
Xinqiang Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Traffic Flow ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Model Combination ◽  
Fuzzy C Means ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Mode Decomposition

scholarly journals Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network

2020 ◽  
Vol 12 (9) ◽  
pp. 3678 ◽  
Author(s):  
Xinqiang Chen ◽  
Jinquan Lu ◽  
Jiansen Zhao ◽  
Zhijian Qu ◽  
Yongsheng Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Empirical Mode Decomposition ◽  
Time Scales ◽  
Ensemble Empirical Mode Decomposition ◽  
Flow Data ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Mode Decomposition

Accurate traffic flow data is crucial for traffic control and management in an intelligent transportation system (ITS), and thus traffic flow prediction research attracts significant attention in the transportation community. Previous studies have suggested that raw traffic flow data may be contaminated by noises caused by unexpected reasons (e.g., loop detector damage, roadway maintenance, etc.), which may degrade traffic flow prediction accuracy. To address this issue, we proposed an ensemble framework via ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) to predict traffic flow under different time intervals ahead. More specifically, the proposed framework firstly employed the EEMD model to suppress the noises in the raw traffic data, which were then processed to predict traffic flow at time steps under different time scales (i.e., 1, 2, and 10 min). We verified our model performance on three loop detectors’ data, which were supported by the Department of Transportation, Minnesota. The research findings can help traffic participants collect more accurate traffic flow data and thus benefits transportation practitioners by helping them to make more reasonable traffic decisions.

scholarly journals Short Term Traffic Flow Prediction of Urban Road Using Time Varying Filtering Based Empirical Mode Decomposition

2020 ◽  
Vol 10 (6) ◽  
pp. 2038 ◽  
Author(s):  
Yanpeng Wang ◽  
Leina Zhao ◽  
Shuqing Li ◽  
Xinyu Wen ◽  
Yang Xiong
Keyword(s):  
Traffic Flow ◽  
Root Mean Square ◽  
Empirical Mode Decomposition ◽  
Original Data ◽  
Time Varying ◽  
Mean Square ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Mode Decomposition

Short-term traffic flow prediction is important to realize real-time traffic instruction. However, due to the existing strong nonlinearity and non-stationarity in short-term traffic volume data, it is hard to obtain a satisfactory result through the traditional method. To this end, this paper develops an innovative hybrid method based on the time varying filtering based empirical mode decomposition (TVF-EMD) and least square support vector machine (LSSVM). Specifically, TVF-EMD is firstly used to deal with the implied non-stationarity in the original data by decomposing them into several different subseries. Then, the LSSVM models are established for each subseries to capture the linear and nonlinear characteristics embedded in the original data, and the corresponding prediction results are superimposed to obtain the final one. Finally, case studies based on two groups of data measured from an arterial road intersection are employed to evaluate the performance of the proposed method. The experimental results indicate it outperforms the other involved models. For example, compared with the LSSVM model, the average improvements by the proposed method in terms of the indexes of mean absolute error, mean relative percentage error, root mean square error and root mean square relative error are 7.397, 15.832%, 10.707 and 24.471%, respectively.

scholarly journals A Neural Network Based Traffic-Flow Prediction Model

2010 ◽  
Vol 15 (2) ◽  
pp. 269-278 ◽  
Author(s):  
B. Çetiner ◽  
Murat Sari ◽  
Oğuz Borat
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Traffic Flow ◽  
Traffic Flow Prediction ◽  
Flow Prediction

scholarly journals Sunspots Time-Series Prediction Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Guohui Li ◽  
Siliang Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Empirical Mode Decomposition ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Ensemble Empirical Mode Decomposition ◽  
Wavelet Neural Network ◽  
Hybrid Prediction ◽  
Mode Decomposition

The sunspot numbers are the major target which describes the solar activity level. Long-term prediction of sunspot activity is of great importance for aerospace, communication, disaster prevention, and so on. To improve the prediction accuracy of sunspot time series, the prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and wavelet neural network (WNN) is proposed. First, the sunspot time series are decomposed by CEEMD to obtain a set of intrinsic modal functions (IMFs). Then, the IMFs and residuals are reconstructed to obtain the training samples and the prediction samples, and these samples are trained and predicted by WNN. Finally, the reconstructed IMFs and residuals are the final prediction results. Five kinds of prediction models are compared, which are BP neural network prediction model, WNN prediction model, empirical mode decomposition and WNN hybrid prediction model, ensemble empirical mode decomposition and WNN hybrid prediction model, and the proposed method in this paper. The same sunspot time series are predicted with five kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

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