Data driven hybrid fuzzy model for short-term traffic flow prediction

Traffic flow prediction can not only improve the reasonability of the managers' decision-making and road planning effectively, but also provide helpful suggestions for travelers to avoid traffic congestion. In order to further improve the prediction accuracy of traffic flow, this study presents one data driven hybrid model for short-term traffic flow prediction. This hybrid model firstly extracts the periodicity pattern from the traffic flow data, then, constructs the functionally weighted single-input-rule-modules connected fuzzy inference system (FWSIRM-FIS) for the residual data after removing the periodicity pattern from the original data, and finally, generates the final prediction results through integrating the periodicity pattern and the output from the FWSIRM-FIS model. The partial autocorrelation function (PACF) method is adopted to determine the optimal inputs for the data driven FWSIRM-FIS model, and the iterative least square method is utilized to train the parameters of the FWSIRM-FIS. Furthermore, three detailed experiments on traffic flow prediction are made, and comprehensive comparisons with three popular artificial intelligence methods are done to verify the effectiveness and advantages of the proposed hybrid model. According to five comparison indices, the proposed hybrid model can achieve the best prediction performance, although with much less fuzzy rules. The error histograms also verify that the proposed hybrid model has the smallest prediction errors comparing to the three comparative methods. The hybrid approach proposed in this study can also be extended to some other applications which have periodicity patterns, e.g. the traveling time estimate and the electricity load forecasting.