November 30, 2025SustainabilityOpen Access

Predicting Urban Traffic Congestion Through Deterministic and Stochastic Modeling Using LSTM Neural Networks

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This analysis demonstrates high accuracy in predicting urban traffic congestion dynamics, suggesting benefits for intelligent transportation systems.

Urban traffic congestion prediction achieved a mean absolute error of 1.5 vehicles per cycle under various scenarios.
The approach integrates stochastic modeling with real-time monitoring data for improved prediction accuracy.
Simulation modeling was developed using the Matlab Simulink environment, enhancing its applicability in urban settings.
The findings highlight the potential integration of predictive models into intelligent transportation systems for urban planning.

A 2025 study studied this question.

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This analysis demonstrates high accuracy in predicting urban traffic congestion dynamics, suggesting benefits for intelligent transportation systems.