Özet:

The population growth and mobility of modern cities have made efficient management of the transportation systems increasingly critical. According to this, the digital twin concept has become a powerful tool for understanding, managing, and optimizing complex systems by creating a digital reflection of the physical world. Transportation is one of these areas, moreover, with the help of such smart intersections and technological infrastructure, large amounts of data are made up of estimation models which is one of the important elements of digital twins. This study aims to provide an academic understanding of which model should be chosen to have accurate and more effective results during the application of traffic value prediction in smart intersections. Hourly vehicle count data from the arrival branches of two smart intersections in Antalya were utilized for this. This data is separated as training and test data created based on learning machine models including Linear Regression Polynomial Regression, Support Vector Regression (SVR), and Random Forest Regression. Thus, traffic volume estimation was conducted for each branch at smart intersections. Forecasting models were evaluated using Mean Absolute Error (MAE) and Least Squares (𝑅) methods. Accordingly, it was seen that the Random Forest model performed better than the other proposed models.

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