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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\u2010critic and proximal policy optimization in multiple deep neural networks (DNNs), including a state\u2010of\u2010the\u2010art transformer model for effective traffic signal management. Here, a single DRL agent is used, which obtains the spatio\u2010temporal 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\u2010time 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.<\/jats:p>","DOI":"10.1002\/ett.4588","type":"journal-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T07:50:13Z","timestamp":1657957813000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep reinforcement learning based cooperative control of traffic signal for multi\u2010intersection network in intelligent transportation system using edge computing"],"prefix":"10.1002","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2369-4223","authenticated-orcid":false,"given":"Ananya","family":"Paul","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology Indian Institute of Engineering Science and Technology, Shibpur Howrah West Bengal India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sulata","family":"Mitra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology Indian Institute of Engineering Science and Technology, Shibpur Howrah West Bengal India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,7,16]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","unstructured":"PaulA MitraS.Real\u2010time routing for ITS enabled fog oriented VANET. 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