Abstract
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.
Highlights
Reliable and stable data source is an important step to obtain reasonable and accurate results in data-mining, system-modeling and future pattern prediction in transportation system
As we know, because the transportation system is influenced by many external factors, including weather effects, data transmission and communication problems, the original traffic data will frequently be interfered during the detection process, which will definitely result in the deterioration of traffic flow analyzing and predicting performance
This study introduced a hybrid traffic flow prediction model by combining Ensemble Empirical Mode Decomposition (EEMD) denoising algorithm and Fuzzy C-means Neural Network (FCMNN) to finish single and multi step prediction using traffic volume data detected from three stations of highway network in Minnesota State
Summary
Reliable and stable data source is an important step to obtain reasonable and accurate results in data-mining, system-modeling and future pattern prediction in transportation system. Methods mainly focus on how to apply the Wavelet Decomposition (WD) [13], Butterworth Filter (BF) [14] and Moving Smoothing (MS) algorithm [15] to eliminate noise before implementing various prediction models, such as, Kalman filter [16], a combined method with ARIMA and Support Vector Machine (SVM) [17], neuro network with wavelet [18], [19], and fuzzy-neural network [20] Overall, all these previous works demonstrated that the denoising method used in traffic flow data is an effective approach to enhance prediction performance
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