Deep reinforcement learning based cooperative control of traffic signal for multi‐intersection network in intelligent transportation system using edge computing

Abstract

In the current era, the coordination of traffic flow is hindered by the discrepancy between road infrastructure and the number of vehicles which leads to traffic congestion. One of the widely used strategies to mitigate traffic congestion is to control traffic signals with the help of deep reinforcement learning (DRL) in edge computing based intelligent transportation system. This article provides a comprehensive analysis of the most recent DRL algorithms, advantage actor‐critic and proximal policy optimization in multiple deep neural networks (DNNs), including a state‐of‐the‐art transformer model for effective traffic signal management. Here, a single DRL agent is used, which obtains the spatio‐temporal information of the traffic to identify traffic patterns from complex intersection environments. The agent uses this information as the input to the DNNs and then applies the algorithms to retrieve the essential parameters of DNN to seek an optimal action selection policy to mitigate congestion. Different real‐time maps and small city networks are explored here to determine which DNN is best suited for traffic congestion management. The simulation study reveals that both the algorithms significantly outperform the baseline. The transformer model gives the best result when compared to other DNNs. The transformer model decreases average waiting time by 96.16%, implying that it has a higher capability of dealing with congested environments.

Research on reliability mapping of 5G low orbit constellation network slice based on deep reinforcement learning

“…The 5G low orbit constellation network slice reliability mapping model based on Section " 5G low orbit constellation network slice reliability mapping model ", taking into account the formula ( 4 ) 5G low orbit constellation network slice reliability mapping model parameter set contain continuous variables such as physical host resource utilization, and the large number of physical host nodes in the production environment will lead to the explosion of 5G low orbit constellation network state space, resulting in reduced reliability of network slice mapping. Therefore, based on the certain decision-making ability of deep reinforcement learning 17 , a reliability mapping method of 5G low orbit constellation network slice based on DQN (deep reinforcement learning) is proposed, and a deep reinforcement network is constructed to fit the 5G low orbit constellation network state behavior value function 18 , to solve the state space explosion problem in the reliability mapping process of 5G low orbit constellation network slice. This algorithm constructs a neural network with a weight of , such that , of which, is the parameter set of reliability mapping model for 5G low orbit constellation network slice, that is, the relevant parameters of formula ( 4 ), is action to perform reliability mapping for.…”

Active integration algorithm of vehicle road cooperative signal based on deep reinforcement learning

“…It is also an extremely important parameter index for the optimization of intersection signal control. In the actual traffic suspension handling process, it is not only necessary to accurately analyze the queuing status of each flow direction at the current intersection [10], but also to adaptively set specific signal control strategies in combination with the current queue length parameters. It can be seen that it is very necessary to actively integrate and process the vehicle road cooperative signal.…”