Investigation of Favorable Neural Network Methods to Estimate Traffic Components

Authors : Sedat OZCANAN

Pages : 377-383

Doi:10.24012/dumf.1219818

View : 75 | Download : 122

Publication Date : 2023-06-20

Article Type : Research Paper

Abstract :Neural networks provide the opportunity to estimate specific components of engineering problems. They are decomposed complex problems into different parts. Thus, it can be easy to compete with each of them through neural networks. In this paper, it was purposed to estimate the average speed of a 6-line road’s cross-section by observed traffic variables, such as numbers of vehicles and occupancy values, using radial basis function neural network insert ignore into journalissuearticles values(RBFNN);, generalized regression neural network insert ignore into journalissuearticles values(GRNN); and the feed-forward back propagation neural network insert ignore into journalissuearticles values(FFBPNN); models. A comparison was fulfilled between different neural networks and checked against multivariate linear regression insert ignore into journalissuearticles values(MVLR);, a conventional statistical model. After each simulation of neural networks, results show that different forecasts were obtained under the same conditions. The best forecasting is made by FFBPNN, GRNN, and RBFNN, respectively. When compared with multivariate linear regression insert ignore into journalissuearticles values(MVLR);, FFBPNN performs better than MVLR, but GRNN and RBFNN perform lower than it.
Keywords : Trafik bileşenlerini tahmin etme, Yapay sinir ağı, FFBPNN, RBFNN, GRNN, MVLR