PREDICTION OF VEHICLE FLOW USING DECISION TREE UDC 656.078, 656.001
Abstract
This paper explores the traffic flow at the intersection of the ring road of Tashkent city with Bogishamol Street. The study focuses on the movement of traffic and its dynamic indicators, such as intensity, density, and speed, which were studied and reprocessed. The main problem addressed in the research was forecasting traffic flow using decision trees, and based on this solution, issues related to traffic management were considered. Alongside this, an analysis of factors affecting traffic flow was conducted, and suggestions for their reduction were proposed. The analysis revealed that special attention is currently being paid to the development of areas such as machine learning, neural networks, and intelligent transportation systems, which are actively being implemented in the transportation sector. Within these areas, an analysis of algorithms, methods, and models of machine learning was conducted. The analysis showed that models such as decision trees, random forests, and gradient boosting are widely used for traffic flow prediction. In this work, a decision tree was also used to develop a model for predicting traffic flow on Bogishamol Street in Tashkent city, which showed good results. The coefficient of determination was used to evaluate this indicator, which showed an accuracy of 92%. This indicates the good predictive value of this model.
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