Abstract
For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.
Highlights
Reliable and accurate real-time traffic flow prediction is crucial for intelligent transportation systems (ITSs)
Comparing Model 8 (CEEMDAN-improved weighted permutation entropy (IWPE)-least-squares support vector machine (LSSVM)) to the proposed model, the mean absolute error (MAE) and root mean square error (RMSE) of the latter are improved by 75.39% and 75.40%, respectively, more than two times as much as the former compared to the CEEMDAN-IWPE-gray wolf optimizer (GWO)-LSSVM model, which indicates that the GWO-optimized LSSVM model can greatly increase prediction precision
We proposed an improved hybrid model of CEEMDAN with IWPE for raw traffic data decomposition and GWO-optimized LSSVM for short-term highway traffic flow prediction
Summary
Reliable and accurate real-time traffic flow prediction is crucial for intelligent transportation systems (ITSs). The EMD method decomposes nonlinear and nonstationary signals into a finite number of intrinsic mode functions (IMFs) without using any defined functions as the basis, and each IMF component represents the sample characteristics on different time scales [30]; EMD will produce serious “modal mixing” phenomena during the decomposition process To solve this shortcoming, Wu improved on the EMD method and proposed an improved ensemble empirical mode decomposition (EEMD) method [31]. It overcomes the modal mixing problem of EMD and solves the problem of the EEMD decomposition method losing completeness and causing reconstruction errors It can accurately reconstruct the original signal, obtain a better modal separation, and reduce the computational cost; we use the CEEMDAN method to decompose the traffic flow time series to improve the quality of the input data of the prediction model. This study paper is arranged as follows: Section 2 introduces the methods and relevant theories to the research; Section 3 introduces the proposed model for traffic flow prediction and prediction framework proposed in detail; Section 4 verifies the proposed prediction model is effective; Section 5 concludes the study and presents future research opportunities
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