Digital-Twin-Based Deep Reinforcement Learning Approach for Adaptive Traffic Signal Control-Reference-Cited by-同舟云学术

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Digital-Twin-Based Deep Reinforcement Learning Approach for Adaptive Traffic Signal Control

Published:2024-06-15 Issue:12 Volume:11 Page:21946-21953
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Kamal Hani¹^ORCID,Yánez Wendy²,Hassan Sara³^ORCID,Sobhy Dalia⁴^ORCID

Affiliation:

1. Pierce Washington, London, U.K.

2. School of Computer Science, University of Birmingham, Birmingham, U.K.

3. College of Computing, Birmingham City University, Birmingham, U.K.

4. Computer Engineering Department, Arab Academy of Science and Technology and Maritime Transport, Alexandria, Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10551641/10472584.pdf?arnumber=10472584

Reference20 articles.

1. The urban environment and health in a world of increasing globalization: Issues for developing countries;McMichael;Bull. World Health Org.,2000

2. Correlations between short-term mobile monitoring and long-term passive sampler measurements of traffic-related air pollution

3. Multi-Agent Deep Reinforcement Learning for Urban Traffic Light Control in Vehicular Networks

4. Impact of Deep RL-based Traffic Signal Control on Air Quality

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review;Environmental Science and Ecotechnology;2024-07

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