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

Urban traffic congestion leads to significant economic losses, increased air pollution, and reduced quality of life, making its prediction and mitigation a critical task for sustainable urban development. Traditional prediction methods are often not integrated with real-time monitoring data, which limits their practical applicability. To bridge this gap, this paper proposes a novel approach that combines deterministic and stochastic simulation modeling to predict traffic congestion of varying complexity. The approach is based on a simulation model developed in the Matlab Simulink environment. The model uses real data from the AIMS eco software package, which provides real-time traffic monitoring. The simulation experiments demonstrated the dynamics of congestion formation and dissipation under various scenarios. Based on these experiments, a neural network (based on LSTM) was developed and trained on an extended dataset to predict the growth of queue length. The LSTM model achieved high accuracy in predicting queue dynamics, with a mean absolute error (MAE) of 1.5 vehicles for the number of vehicles unable to pass through the intersection per cycle and 0.3 vehicles for the total queue size. The developed model represents an effective tool for analyzing and predicting traffic congestion, thereby providing a scientific foundation for integrating a predictive module into intelligent transportation systems (ITS) such as AIMS eco.