| Abstrakt: |
This article is dedicated to the study of traffic flow forecasting at the intersection of Bogishamol Street in Tashkent, Uzbekistan. In the context of the rapid growth of vehicular traffic and frequent congestion, developing effective forecasting models is a pressing task that will help optimize traffic flow management. The research examines and analyzes various machine learning methods, such as decision trees, random forests, and gradient boosting, for predicting traffic intensity. The data for the models was collected using video cameras installed at the intersection which provided accurate and up-to-date traffic flow information. The main focus of the study is on the comparative analysis of the performance of these methods. The comparison was made using various evaluation metrics, such as the coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE). These metrics allowed for an objective assessment of the accuracy and effectiveness of each method in the context of traffic flow prediction. The results of the study showed that the gradient boosting model demonstrated the best performance among the methods considered. It achieved the highest R2 values and the lowest MSE and MAE values, indicating its high accuracy and ability to adequately predict changes in traffic flows. The decision tree and random forest models also showed good results but were outperformed by gradient boosting in key indicators. The findings have significant practical implications. They can be used to develop intelligent traffic management systems aimed at increasing the capacity of roads and intersections. This, in turn, can help reduce congestion, lower emissions of harmful substances into the atmosphere, and decrease economic costs associated with traffic delays. [ABSTRACT FROM AUTHOR] |
