An Approach for Short Term Traffic Flow Forecasting Based on Fuzzy Logic Control

2013 ◽  
Vol 336-338 ◽  
pp. 438-441
Author(s):  
Wei Dong Dai

In this paper, fuzzy logic control is applied to forecast the short-term traffic flow and traffic guidance. Because of the factors of time correlation and spatial correlation, we construct the short-term traffic flow forecasting model using fuzzy logic control that can handle non-linear plant behavior. In order to find a feasibility way of traffic flow prediction, we deal the combination of time correlation traffic value and space correlation traffic value as the input variables. Considering the real condition, we use triangular and trapezoid membership function to design the belongings relationship. Five fuzzy rules are applied in the control. Last, we use fuzzy logic toolbox to simulate the short term traffic flow forecasting basing on the fuzzy logic control. The system input/output curve result shows that this method can have a good performance for short-term traffic flow prediction.

2013 ◽  
Vol 671-674 ◽  
pp. 2866-2869 ◽  
Author(s):  
Lei Yang ◽  
Wei Dong Dai

In this paper, genetic neural network is applied to forecast the short-term traffic flow and traffic guidance. Because of the factors of time correlation and spatial correlation, we construct the short-term traffic flow forecasting model using back-propagation neural network that has the function of arbitrary nonlinear function approximation. In order to find proper initial values of the neural network weights and threshold quickly, a combination of neural network prediction method is presented. This method utilizes genetic algorithm to choose the initial weights and threshold, and uses L-M algorithm to train sample, which can enhance the global convergence rate. Trained network is used for short-term traffic flow prediction with mean square error as the forecast performance evaluation. The results show that the performance of genetic neural network is better than a separate BP neural network for short-term traffic flow prediction.


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