Article · NEWS OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN · March 27, 2026

DEVELOPMENT AND COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR URBAN TRAFFIC PREDICTION

Aigul Mimenbayeva, R.D. Turebayeva, Т.Т. Ospanova, А.B. Aruova, A A Naizagarayeva

Abstract

Urban traffic congestion is a major issue in modern cities. Rapid urbanization and increasing vehicle numbers intensify this problem. Accurate traffic prediction is essential for reducing congestion. It also improves road safety and supports intelligent transportation systems. This study develops and compares machine learning models for urban traffic classification. The dataset includes traffic volume indicators and related parameters. Data preprocessing involved exploratory data analysis, feature extraction, and normalization. Three algorithms were implemented: Logistic Regression, Support Vector Machine (SVC), and Random Forest. The experimental results show that Logistic Regression achieved an overall accuracy of 0.87, with weaker performance in the “low” traffic class. The SVC model slightly improved the results, reaching an accuracy of 0.88 and demonstrating better balance across classes. The Random Forest model showed the highest performance, achieving an overall accuracy of 0.99 and weighted F1-score of 0.99, with near-perfect precision and recall for all traffic categories. The findings indicate that ensemble learning methods, particularly Random Forest, provide superior accuracy and reliability for urban traffic prediction tasks. The proposed model can be integrated into real-time traffic management systems to enhance decision-making and improve urban mobility.

Traffic Prediction and Management TechniquesData Mining and Machine Learning ApplicationsArtificial Intelligence in Healthcareurban traffic predictiontraffic congestionrandom forest

DEVELOPMENT AND COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR URBAN TRAFFIC PREDICTION

Abstract

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