Traffic congestion forecasting using machine learning methods

Traffic congestion forecasting using machine learning methods

Background. This study develops a comprehensive approach to traffic congestion forecasting using synthetic data that simulates the dynamics of urban traffic. A hybrid methodology is proposed that combines time series analysis and deep learning, which is highly relevant for modeling nonlinear depende...

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Bibliographic Details
Main Authors: Ramil R. Zagidullin, Almaz N. Khaybullin
Format: Article
Language:English
Published: Science and Innovation Center Publishing House 2025-06-01
Series:International Journal of Advanced Studies
Subjects:
traffic congestion
vehicle
neural network
machine learning
forecasting
Online Access:https://ijournal-as.com/jour/index.php/ijas/article/view/347
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