Traffic Flow Forecasting Based on Fuzzy-Neural

Fuzzy systems can be excellently used to represent human knowledge. Traffic technology is a science where this property of fuzzy logic can be used very well because it is hard to make mathematical models due to human influences and complex connections between input parameters. This paper applies a novel Fuzzy Cerebella-Model-Articulation-Controller (FCMAC) into univariate time-series forecasting and investigates its performance in comparison to established techniques such as the Box-Jenkin´s ARIMA model. Experimental results from Pudong New Strict in Shanghai traffic flow data reveal that the FCMAC model yielded lower errors for certain data sets. The conditions under which the FCMAC model emerged superior are discussed. Furthermore, we show how neural networks can be used to improve the performance of the system.