Deep Reinforcement Learning for Traffic Signal Control
The increasing demand for mobility in cities and beyond presents challenges for traffic engineering. As a solution to these challenges, we can use the power of artificial intelligence and reinforcement learning. These scientific branches can enable us to use the current infrastructure more efficiently, thus reducing the environmental impact and increasing the comfort of drivers. In this paper, using the Ingolstadt and Cologne benchmarks from RESCO, the authors compare their agent, which is based on Perceiver with IDQN (Independent Deep Q-Network), to the conventional method of Max-pressure and random times for green signals.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
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Supplemental Notes:
- © 2023 The Author(s). Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
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Authors:
- Skuba, Michal
- Janota, Aleš
- Kuchár, Pavol
- Malobický, Branislav
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Conference:
- TRANSCOM 2023: 15th International Scientific Conference on Sustainable, Modern and Safe Transport
- Location: Mikulov , Czech Republic
- Date: 2023-5-29 to 2023-5-31
- Publication Date: 2023
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; Maps; References;
- Pagination: pp 954-958
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Serial:
- Transportation Research Procedia
- Volume: 74
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 2352-1465
- Serial URL: http://www.sciencedirect.com/science/journal/23521465/
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Alternatives analysis; Artificial intelligence; Machine learning; Traffic signal control systems
- Subject Areas: Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01916286
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 22 2024 9:39AM