EcoMRL: Deep reinforcement learning-based traffic signal control for urban air quality-Reference-Cited by-同舟云学术

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EcoMRL: Deep reinforcement learning-based traffic signal control for urban air quality

Published:2024-06-12 Issue: Volume: Page:1-10
ISSN:1556-8318
Container-title:International Journal of Sustainable Transportation
language:en
Short-container-title:International Journal of Sustainable Transportation

Author:

Jung Jaeeun¹^ORCID,Kim Inhi¹^ORCID,Yoon Jinwon²^ORCID

Affiliation:

1. Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

2. Mechanical Engineering Research Institute, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

Funder

National Research Foundation of Korea

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15568318.2024.2364728

Reference44 articles.

1. Computing optimum traffic signal cycle length considering vehicle delay and fuel consumption

2. Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control

3. The traffic signal control problem for intersections: a review

4. Fast Facts on Transportation Greenhouse Gas Emissions. 2022. https://www.epa.gov/greenvehicles/fast-facts-transportation-greenhouse-gas-emissions

5. Effects of Smart Traffic Signal Control on Air Quality

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