Abstract
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.
Highlights
As one of the key technologies of real-time traffic signal control, traffic assignment, route guidance, and other functions in the intelligent transportation system, short-term traffic flow prediction has always been the research focus
This paper proposes a novel time varying filtering based empirical mode decomposition (TVF-EMD) algorithm, which vividly describes the time-varying characteristics of data and overcomes the occurrence of mode mixing [27]
This paper simultaneously combines the advantages of these two models and builds a new hybrid forecasting model, i.e., TVF-EMD-least square support vector machine (LSSVM)
Summary
As one of the key technologies of real-time traffic signal control, traffic assignment, route guidance, and other functions in the intelligent transportation system, short-term traffic flow prediction has always been the research focus. The decomposition-based methods have become the research focus [21] This kind of hybrid model could use the data processing models to address the nonlinear and non-stationary features in the data, and the forecasting accuracy could be enhanced. Duo et al [24] proposed a hybrid forecasting method of short-term traffic volume based on EMD and the improved SVM. Tang et al [25] adopted a new hybrid model for traffic volume prediction by using the combination of EEMD and SVM The results showed this model had superior performance over the single SVM. The LSSVM model is adopted for each subsequence to perform the final prediction On this basis, five evaluation indexes including the mean absolute error, mean relative percentage error, root mean square error, root mean square relative error and equal coefficient are used to systematically evaluate the forecasting results. The structure and procedure of the proposed method are described in detail; In Section 3, two case studies are performed and the effectiveness of the proposed method is analyzed and discussed; In Section 4, some conclusions are summarized
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
