Intelligent Traffic Management(ITM)has progressively developed into a critical component of modern transportation networks,significantly enhancing traffic flow and reducing congestion in urban environments.This resear...Intelligent Traffic Management(ITM)has progressively developed into a critical component of modern transportation networks,significantly enhancing traffic flow and reducing congestion in urban environments.This research proposes an enhanced framework that leverages Deep Q-Learning(DQL),Game Theory(GT),and Stochastic Optimization(SO)to tackle the complex dynamics in transportation networks.The DQL component utilizes the distribution of traffic conditions for epsilon-greedy policy formulation and action and choice reward calculation,ensuring resilient decision-making.GT models the interaction between vehicles and intersections through probabilistic distributions of various features to enhance performance.Results demonstrate that the proposed framework is a scalable solution for dynamic optimization in transportation networks.展开更多
基金the Deanship of Scientific Research at King Khalid University for funding this research through the Large Group Research Project under grant number RGP2/324/46.
文摘Intelligent Traffic Management(ITM)has progressively developed into a critical component of modern transportation networks,significantly enhancing traffic flow and reducing congestion in urban environments.This research proposes an enhanced framework that leverages Deep Q-Learning(DQL),Game Theory(GT),and Stochastic Optimization(SO)to tackle the complex dynamics in transportation networks.The DQL component utilizes the distribution of traffic conditions for epsilon-greedy policy formulation and action and choice reward calculation,ensuring resilient decision-making.GT models the interaction between vehicles and intersections through probabilistic distributions of various features to enhance performance.Results demonstrate that the proposed framework is a scalable solution for dynamic optimization in transportation networks.
