- El-Cezeri
- Vol: 7 Issue: 3
- Tali Yollar için Kısa Vadeli Trafik Hacminin Yapay Sinir Ağlarıyla Belirlenmesi
Tali Yollar için Kısa Vadeli Trafik Hacminin Yapay Sinir Ağlarıyla Belirlenmesi
Authors : Abdulgazi Gedik
Pages : 1496-1508
Doi:10.31202/ecjse.773088
View : 8 | Download : 5
Publication Date : 2020-09-30
Article Type : Research
Abstract :The forecasting of merging road traffic volume is one of the critical issues for the main networks of traffic-congestion suffering cities. Artificial neural network (ANN) – used in many disciplines varying from economy to different engineering applications such as sales forecasting, industrial process control, customer research, data validation, risk management, target marketing and civil engineering – could be a promising solution to this issue. Providing a higher forecasting accuracy based on past traffic data, ANN has become very popular in transportation engineering for the last 30 years. In this paper, the main goal was to predict the short-term traffic volume of a connection road leading to one of Istanbul’s Bosphorous Bridge in Turkey by the three different implementations of ANN. These were Feed Forward Back Propagation (FFBP), Generalized Regression Neural Network (GRNN) and Radial Based Function (RBF). Then, obtained results were compared with each other and the result of Multi Linear Regression (MLR) method.Keywords : Artificial neural network, traffic volume, traffic flow, short-term prediction