Short Term Traffic Flow Prediction of Urban Road Using Time Varying Filtering Based Empirical 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 develo...

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Bibliographic Details
Published inApplied sciences Vol. 10; no. 6; p. 2038
Main Authors Wang, Yanpeng, Zhao, Leina, Li, Shuqing, Wen, Xinyu, Xiong, Yang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2020
Subjects
Accuracy
Algorithms
Decomposition
Errors
Filtration
forecasting accuracy
lssvm
Nonlinearity
Optimization
Predictions
Short term
short-term traffic flow prediction
Signal processing
Statistical methods
Support vector machines
Traffic assignment
Traffic control
Traffic flow
Traffic volume
tvf-emd
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10062038