This paper explores the traffic flow at the intersection of the ring road of Tashkentcity 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 problemaddressed 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 affectingtraffic flow was conducted, and suggestions for their reduction were proposed. The analysis revealedthat 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 thetransportation sector. Within these areas, an analysis of algorithms, methods, and models of machinelearning was conducted. The analysis showed that models such as decision trees, random forests, andgradient boosting are widely used for traffic flow prediction. In this work, a decision tree was alsoused to develop a model for predicting traffic flow on Bogishamol Street in Tashkent city, whichshowed good results. The coefficient of determination was used to evaluate this indicator, whichshowed an accuracy of 92%. This indicates the good predictive value of this model.
| Mualliflar | Rasulmukhamedov, Mahamadaziz, Tashmetov, Komoliddin, Tashmetov, Timur |
|---|---|
| Jurnal | Инновацион технологиялар |
| Nashr sanasi | 2024-12-26 |
| Jild | 54 |
| Son | 02 |
| Til | Ingliz |
traffic flow, congestion, prediction, algorithm, model, decision tree, machine learning models, coefficient of determination, entropy., транспортный поток, перегруженность, прогнозирование, алгоритм, модель, дерево решений, модели машинного обучения, коэффициент детерминации, энтропия., Transport oqimi, tirbandlik, bashorat, algoritm, model, qaror daraxti, mashinani oʻrgatish modellari, determinatsiya koeffitsiyenti, entropiya.
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