Abstract
Forecasting short-term traffic flow is a key task of intelligent transportation systems, which can influence the traveler behaviors and reduce traffic congestion, fuel consumption, and accident risks. This paper proposes a fuzzy wavelet neural network (FWNN) trained by improved biogeography-based optimization (BBO) algorithm for forecasting short-term traffic flow using past traffic data. The original BBO is enhanced by the ring topology and Powell's method to advance the exploration capability and increase the convergence speed. Our presented approach combines the strengths of fuzzy logic, wavelet transform, neural network, and the heuristic algorithm to detect the trends and patterns of transportation data and thus has been successfully applied to transport forecasting. Other different forecasting methods, including ANN-based model, FWNN-based model, and WNN-based model, are also developed to validate the proposed approach. In order to make the comparisons across different methods, the performance evaluation is based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R). The performance indexes show that the FWNN model achieves lower RMSE and MAPE, as well as higher R, indicating that the FWNN model is a better predictor.
Highlights
In the transportation area, attention is paid to construct physical system capacity and to improve operational efficiency and integration. e intelligent transportation system (ITS) applying the advanced sensing, analysis, control, and communications technologies aims to ease traffic congestion, improve traffic management, and reduce environmental impact
E models were implemented in the MATLAB 2015a environment. e simulation results were obtained and are presented in Figures 6 and 7 and Table 3. e time series of actual and forecasting values obtained by the WNN-based model, fuzzy wavelet neural network (FWNN)-biogeography-based optimization (BBO)-based model, FWNN-iBBO-based model, FWNN-based model, and Artificial neural network (ANN)-based model are compared in Figure 6. e nearly perfect agreement between the trends in the plots of the actual and forecasting values indicates that the FWNN-BBO-based model is the most suitable model
E comparison between actual values and forecasting values obtained by FWNN-BBO-based model and FWNNiBBO-based model are shown in Figure 7. e figure presents the scatter diagrams that illustrate the degree of correlation between forecasting values and actual values
Summary
Attention is paid to construct physical system capacity and to improve operational efficiency and integration. e intelligent transportation system (ITS) applying the advanced sensing, analysis, control, and communications technologies aims to ease traffic congestion, improve traffic management, and reduce environmental impact. Erefore, using the short-term traffic forecasting models based on the classical mathematical methods such as statistical techniques, the precision of forecast cannot meet the requirement of real-time transportation management systems [8]. Artificial neural network (ANN) is certainly the most widely used one for forecasting the transportation data, especially the short-term traffic flow forecasting [9]. A traffic flow prediction model based on wavelet transform and fuzzy neural network was proposed in optimal control of the intelligent traffic system [13]. A traffic flow prediction model based on the fuzzy c-mean clustering method (FCM) and the neural network was proposed [15]. E merit of BBO algorithm, the wavelet transform, the fuzzy system, and the success of ANNs have encouraged us to combine these techniques for forecasting traffic flow.
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