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Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biog 时间:2018-10-13 21:27   来源:未知   作者:admin   点击: 次   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.
1. Introduction
  In the transportation area, attention is not only paid to construct physical system capacity but also to improve operational efficiency and integration. The 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. As ITSs have been widely developed throughout the world, how to improve the ability to predict traffic flow in the short term (within the next one hour, e.g., 5 min, 10 min, and 15 min) has been getting much attention from researchers. Short-term traffic flow forecasts can support proactive transportation management and comprehensive traveler information service. The goal is to predict traffic conditions in a transport network based on its past behavior. Several methods have been implemented for the short-term traffic flow forecasts. These methods can be grouped into two categories: (1) methods based on statistical techniques and (2) methods based on artificial intelligence techniques.
  With the assumption that the characteristics of forecasting traffic flow data are similar to historical and current flow data, forecasting models based on statistical techniques utilize mathematical statistics to deal with the pervious and current measurements of traffic flow and forecast the future values of traffic flow. Several well-known classical time-series approaches are the Box–Jenkins method [1], autoregressive integrated moving average (ARIMA) model [2, 3], seasonal ARIMA (SARIMA) [4, 5], and a number of variant forms of ARIMA models. Afterwards, researchers found that ARIMA model cannot tackle the problem of forecasting the extreme volume values [6, 7]. Moreover, the short-term traffic flow forecast is more easily affected by the stochastic interferential factors than the long-term one, the uncertainty is greater, and the disciplinarian laws are less obvious. Therefore, 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].
As for the use of artificial intelligence-based techniques, several approaches have been applied to the task of traffic forecasting. Artificial neural network (ANN) is certainly the most widely used one for forecasting the transportation data, especially the short-term traffic flow forecasting [9]. It is a supervised learning algorithm that can be trained to learn a function between input features and the output, which is represented by the target to be predicted. The most widely used ANN-based models in short-term traffic flow forecasting are multilayer perception (MLP), backpropagation neural networks (BPNN), and radial basis function neural networks (RBFNN) [10]. The pros and cons of these models have been addressed in the literature [7, 11, 12].