Abstract
Traffic flow prediction plays an important role in intelligent transportation system (ITS). However, due to the randomness and complex periodicity of traffic flow data, traditional prediction models often fail to achieve good results. On the other hand, external disturbances or abnormal detectors will cause the collected traffic flow data to contain noise components, resulting in a decrease in prediction accuracy. In order to improve the accuracy of traffic flow prediction, this study proposes a mixed traffic flow prediction model VMD-WD-LSTM using variational mode decomposition (VMD), wavelet threshold denoising (WD), and long short-term memory (LSTM) network. Firstly, we decompose the original traffic flow sequence into K components through VMD and determine the number of components K according to the sample entropy of different K values. Then, each component is denoised by wavelet threshold to obtain the denoised subsequence. Finally, LSTM is used to predict each subsequence, and the predicted values of each subsequence are combined into the final prediction results. In addition, the performance of the proposed model and the latest traffic flow prediction model is compared on the several well-known public datasets. The empirical analysis shows that the proposed model not only has good prediction accuracy but also has superior robustness.
Highlights
With the rapid development of cities and the rapid increase of urban population, the number of vehicles on urban roads is increasing. erefore, the increased traffic pressure on urban roads has caused more and more serious problems, such as traffic accidents and traffic pollution, and road congestion has become an important factor affecting the quality of daily life of residents
Wavelet denoising is a commonly used denoising method in the field of traffic flow prediction. e Kalman filter model based on wavelet decomposition has been used for short-term traffic flow prediction. e empirical results show that the combination of wavelet decomposition and Kalman filter can reduce the impact of noise on prediction to a certain extent [2]
In the fuzzy neural network prediction model proposed by Xiao et al, wavelet decomposition was used to smooth historical traffic flow data, and the results show that wavelet denoising can significantly improve the prediction accuracy [4]
Summary
A Hybrid Model for Short-Term Traffic Flow Prediction Based on Variational Mode Decomposition, Wavelet Threshold Denoising, and Long Short-Term Memory Neural Network. Due to the randomness and complex periodicity of traffic flow data, traditional prediction models often fail to achieve good results. External disturbances or abnormal detectors will cause the collected traffic flow data to contain noise components, resulting in a decrease in prediction accuracy. In order to improve the accuracy of traffic flow prediction, this study proposes a mixed traffic flow prediction model VMD-WD-LSTM using variational mode decomposition (VMD), wavelet threshold denoising (WD), and long short-term memory (LSTM) network. The performance of the proposed model and the latest traffic flow prediction model is compared on the several well-known public datasets. The performance of the proposed model and the latest traffic flow prediction model is compared on the several well-known public datasets. e empirical analysis shows that the proposed model has good prediction accuracy and has superior robustness
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