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Traffic flow control using multi-agent reinforcement learning

Zeynivand, A ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1016/j.jnca.2022.103497
  3. Publisher: Academic Press , 2022
  4. Abstract:
  5. One of the technologies based on information technology used today is the VANET network used for inter-road communication. Today, many developed countries use this technology to optimize travel times, queue lengths, number of vehicle stops, and overall traffic network efficiency. In this research, we investigate the critical and necessary factors to increase the quality of VANET networks. This paper focuses on increasing the quality of service using multi-agent learning methods. The innovation of this study is using artificial intelligence to improve the network's quality of service, which uses a mechanism and algorithm to find the optimal behavior of agents in the VANET. The result indicates that the proposed method is more optimal in the evaluation criteria of packet delivery ratio (PDR), transaction success rate, phase duration, and queue length than the previous ones. According to the evaluation criteria, TSR 6.342%, PDR 9.105%, QL 7.143%, and PD 6.783% are more efficient than previous works. © 2022 Elsevier Ltd
  6. Keywords:
  7. Artificial intelligence ; Multi-way systems ; Reinforcement learning and Q-learning ; Service quality ; Smart control of traffic lights ; Learning systems ; Multi agent systems ; Quality control ; Quality of service ; Traffic control ; Travel time ; Evaluation criteria ; Multi-way system ; Q-learning ; Queue lengths ; Reinforcement learnings ; Smart control ; Smart control of traffic light ; Traffic light ; Reinforcement learning
  8. Source: Journal of Network and Computer Applications ; Volume 207 , 2022 ; 10848045 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S1084804522001394