Hybrid short-term traffic flow prediction model of intersections based on improved complete ensemble empirical mode decomposition with adaptive noise

Tian, Xiujuan; Yu, Duk Jun; Xing, Xue; Wang, Shiguang; Wang, Zhuorui

doi:10.1177/1687814019841819

Cited by 12 publications

(12 citation statements)

References 31 publications

(39 reference statements)

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“…The ICEEMDAN algorithm is a further improvement based on the EEMD and CEEM-DAN algorithms and has wide applicability in numerous fields. Experiments show that ICEEMDAN solves the possible residual noise in the EEMD algorithm and the possible false modes problem in the CEEMDAN algorithm, effectively improving the noise reduction effect [34][35][36]. The specific steps of the ICEEMDAN algorithm are as follows:…”

Section: Iceemdanmentioning

confidence: 99%

Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

Zhou

Chen

2021

Sustainability

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Effective carbon pricing policies have become an effective tool for many countries to encourage emission reduction. An accurate carbon price prediction model is helpful for the implementation of energy conservation and emission reduction policies and the decision-making of governments and investors. However, it is difficult for a single prediction model to achieve high prediction accuracy because of the high complexity of the carbon price series. Many studies have proved the nonlinear characteristics of carbon trading prices, but there are very few studies on the chaotic nature of carbon price series. As a consequence, this paper proposes an innovative hybrid model for carbon price prediction. A decomposition-reconstruction-prediction-integration scheme is designed to predict carbon prices. Firstly, several intrinsic mode functions (IMFs) and one residue were obtained from the raw data decomposed by ICEEMDAN. Next, the decomposed subsection is reconstructed into a new sequence according to the calculation results by the Lempel-Ziv complexity algorithm. Then, considering the chaotic characteristics of sequence, the input variables of the models are determined through the phase space reconstruction (PSR) algorithm combined with the partial autocorrelation function (PACF). Finally, the Sparrow search algorithm (SSA) is introduced to optimize the extreme learning machine (ELM) model, which is applied in the carbon price prediction for the purpose of verifying the validity of the proposed combination model, which is applied to the pilots of Hubei, Beijing, and Guangdong. The empirical results show that the combination model outperformed the 13 other models in predicting accuracy, speed, and stability. The decomposition-reconstruction-prediction-integration strategy is a method for predicting the carbon price efficiently.

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Section: Iceemdanmentioning

confidence: 99%

Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

Zhou

Chen

2021

Sustainability

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“…There are many factors affecting prediction accuracy, such as data quality, data characteristics, and model selection, etc. However, the quality of traffic volume data is one of the main factors [26]. Therefore, the processing of the abnormal data including missing data and erroneous data appears to be crucial in traffic volume prediction [30].…”

Section: Data Processingmentioning

confidence: 99%

“…In order to analyze and evaluate the forecasting performance of the proposed model, five commonly used evaluation indexes including mean absolute error (MAE), mean relative percentage error (MRPE), root mean square error (RMSE), root mean square relative error (RMSRE) and equal coefficient (EC) were used in the study [26,29]. Their specific definitions are given by:…”

Section: Evaluation Criteriamentioning

confidence: 99%

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

et al. 2020

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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.

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“…Experimental results showed a better effect and higher accuracy compared to other models. Tian et al [21] proposed an EMDbased model to predict short-term traffic flow. First, the original traffic flow sequence was decomposed by EMD.…”

Section: Related Workmentioning

confidence: 99%

Mode decomposition based deep learning model for multi-section traffic prediction

2020

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Road traffic prediction plays a vital role in real-time traffic management of an intelligent transportation system (ITS). Many prediction models achieve fine results. However, most ignore the intrinsic characteristics of traffic parameter data and do not consider the spatiotemporal effects of road sections, which can reflect the situation of all road traffic. Therefore, multi-section traffic prediction is still an open problem. In this paper, empirical mode decomposition (EMD) is employed to decompose the information of traffic parameters into many intrinsic mode function (IMF) components, which represent the original road traffic information in periodic and random sequences. Then, by considering the superiority of deep learning in multi-dimensional data processing, which can handle the spatiotemporal effects, a prediction model based on a convolutional neural network (CNN) is proposed to achieve the prediction of periodic and random sequences, whose results are combined to obtain the final prediction. The dataset from the Caltrans Performance Measurement System is used to validate the model. The proposed prediction model is compared to several well-known models, such as PCA-BP, Lasso-BP, and standard CNN. Experiments show that the proposed prediction model achieves higher accuracy.

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Hybrid short-term traffic flow prediction model of intersections based on improved complete ensemble empirical mode decomposition with adaptive noise

Cited by 12 publications

References 31 publications

Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

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

Mode decomposition based deep learning model for multi-section traffic prediction

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