Projet de Fin d'Études : Mémoire de Master

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Traffic Management Optimization in the Presence of Automated Vehicles
(Tassadit, 2025-01-25) Cheima MEZDOUR
Traffic congestion on freeways is a significant issue today due to the high volume of vehicles. This congestion impacts traffic flow and can lead to severe delays. Effective traffic management methods and solutions are essential to mitigate this problem and enhance safety for all road users. One specific issue in freeway traffic congestion is the problem of ramp metering. This occurs when vehicles merge from an entrance ramp onto the main freeway. Any malfunction or inefficiency in the traffic light system at these ramps can cause significant congestion and even lead to accidents. By implementing a sophisticated decision-making system that follows a realworld network, we can develop better policies to address this issue. This approach can optimize traffic flow, reduce congestion, and improve overall road safety. We proposed a deep q-learning algorithm to address this problem, which guarantees better learning policies and adapt to changing traffic conditions in real-time, learning optimal actions to take at different states of traffic flow in complex environments. Deep Q-Networks(DQNs) can be used across multiple ramps, so this can improve the scalability of the model. So during this study, we will present the performance of this algorithm compared to traditional systems in improving the efficiency of traffic control.