A method of traffic variable estimation based on neuro-fuzzy on urban road

This paper studies traffic variable estimation, and presents a method of estimation for the number of vehicle waiting for queue (NVWQ) based on neuro-fuzzy at urban intersection. we present results of training the neural network for a detectorized intersection in Changsha City. The accuracy of NVWQ estimation using the fuzzy neural networks approaches is more than 90%. The fuzzy neural networks have advantages of both fuzzy expert systems (knowledge representation) and artificial neural networks (learning). The fuzzy neural networks can be trained successfully to estimate NVWQ for different traffic flow patterns and different conditions intersection. This greatly reduces a lot of effort of extracting traffic expert's knowledge into fuzzy if-then rules. All we have to do is to present training data to the network which will figure out its own rules through internal representation. In traffic signal control system, detection of traffic variables at intersection, such as NVWQ is very important and is the basic input data to determine signal timing.