부산대학교 도서관

The nonstationary traffic flow prediction is developed by using Bayesian approach in which the state conditioned upon the available measurement data is computed recursively. Both curvilinear regression approach and AR model for traffic flow forecasting are investigated. The parameters of above two approaches are adaptively updated by using Bayesian method. Adaptive neural networks (ATNN) is also adopted for the training procedure (Park et al., 1991a), which adapts the weights of a trained layered perceptron artificial neural network to training the data originated from a slowly varying nonstationary traffic flow processing. Through the simulation to traffic flow data got from a detector at a specific zone (#Z340) of Northern State Parkway (NY), the procedure is shown to adapt to new training data that is in conflict with earlier training data with least influence on neural network's response to previous data.