Traffic Management Optimization in the Presence of Automated Vehicles

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dc.contributor.authorCheima MEZDOUR
dc.date.accessioned2025-01-21T21:25:59Z
dc.date.available2025-01-21T21:25:59Z
dc.date.issued2025-01-21
dc.description.abstractTraffic 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 real-world 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 can be used across multiple ramps, so this can improve the scalabil ity 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.
dc.identifier.urihttps://dspace.estin.dz/handle/123456789/33
dc.language.isoen
dc.publisherTassadit
dc.subjectTraffic congestion
dc.subjectFreeways
dc.subjectTraffic flow
dc.subjectRamp metering
dc.subjectTraffic man agement
dc.subjectDeep Q-Network
dc.subjectDecision-making system
dc.subjectReal-world network
dc.subjectTraffic control
dc.subjectOptimization
dc.subjectScalability
dc.subjectRoad safety
dc.subjectAdaptive policies
dc.subjectTraffic conditions
dc.titleTraffic Management Optimization in the Presence of Automated Vehicles
dc.typeThesis

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