Collaborative Traffic Signal Automation Using Deep Q-Learning Article Swipe
Muhammad Ahmed Hassan
,
Mourad Elhadef
,
Muhammad Usman Ghani Khan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3331317
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3331317
Multi-agent deep reinforcement learning (MDRL) is a popular choice for multi-intersection traffic signal control, generating decentralized cooperative traffic signal strategies in specific traffic networks. Despite its widespread use, current MDRL algorithms have certain limitations. Firstly, the specific multi-agent settings impede the transferability and generalization of traffic signal policies to different traffic networks. Secondly, existing MDRL algorithms struggle to adapt to a varying number of vehicles crossing the traffic networks. This paper introduces a novel Cooperative Multi-Agent Deep Q-Network (CMDQN) for traffic signal control to alleviate traffic congestion. We have considered innovative features such as signal state at the preceding junction, the distance between junctions, visual features, and average speed. Our CMDQN applies a Decentralized Multi-Agent Network (DMN), employing a Markov Game abstraction for collaboration and state information sharing between agents to reduce waiting times. Our work employs Reinforcement Learning (RL) and a Deep Q-Network (DQN) for adaptive traffic signal control, leveraging Deep Computer Vision for real-time traffic density information. We also propose an intersection and a network-wide reward function to evaluate performance and optimize traffic flow. The developed system was evaluated through both synthetic and real-world experiments. The synthetic network is based on the Simulation of Urban Mobility (SUMO) traffic simulator, and the real-world network employed traffic data collected from installed cameras at actual traffic signals. Our results demonstrated improved performance across several key metrics when compared to the baseline model, reducing waiting times and improving traffic flow. This research presents a promising approach for cooperative traffic signal control, significantly contributing to the efforts to enhance traffic management systems.
Related Topics
Concepts
Computer science
Reinforcement learning
Intersection (aeronautics)
Traffic flow (computer networking)
Traffic generation model
SIGNAL (programming language)
Deep learning
Real-time computing
Artificial intelligence
Network traffic control
Distributed computing
Machine learning
Computer network
Engineering
Network packet
Aerospace engineering
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2023.3331317
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10311560.pdf
- OA Status
- gold
- Cited By
- 14
- References
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388505113
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388505113Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2023.3331317Digital Object Identifier
- Title
-
Collaborative Traffic Signal Automation Using Deep Q-LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Muhammad Ahmed Hassan, Mourad Elhadef, Muhammad Usman Ghani KhanList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2023.3331317Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/10311560.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10311560.pdfDirect OA link when available
- Concepts
-
Computer science, Reinforcement learning, Intersection (aeronautics), Traffic flow (computer networking), Traffic generation model, SIGNAL (programming language), Deep learning, Real-time computing, Artificial intelligence, Network traffic control, Distributed computing, Machine learning, Computer network, Engineering, Network packet, Aerospace engineering, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 6Per-year citation counts (last 5 years)
- References (count)
-
64Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2892016325, https://openalex.org/W2772329503, https://openalex.org/W6948057359, https://openalex.org/W4210562960, https://openalex.org/W4234103396, https://openalex.org/W1926952688, https://openalex.org/W2002937539, https://openalex.org/W2007933500, https://openalex.org/W2565735610, https://openalex.org/W2809148419, https://openalex.org/W2109199369, https://openalex.org/W2761964598, https://openalex.org/W2032908152, https://openalex.org/W2508626445, https://openalex.org/W3035189581, https://openalex.org/W3015862565, https://openalex.org/W3216884019, https://openalex.org/W2020070197, https://openalex.org/W2134698488, https://openalex.org/W3046369858, https://openalex.org/W2958658968, https://openalex.org/W2928629935, https://openalex.org/W2794842204, https://openalex.org/W2963027910, https://openalex.org/W23993467, https://openalex.org/W2773347164, https://openalex.org/W2625186350, https://openalex.org/W2894849109, https://openalex.org/W2766710212, https://openalex.org/W2480177474, https://openalex.org/W2078895652, https://openalex.org/W4210742101, https://openalex.org/W2074500080, https://openalex.org/W1639167632, https://openalex.org/W6637967152, https://openalex.org/W2145339207, https://openalex.org/W2124657875, https://openalex.org/W6729224713, https://openalex.org/W2498017881, https://openalex.org/W2915117209, https://openalex.org/W2037067712, https://openalex.org/W1998622835, https://openalex.org/W1969758122, https://openalex.org/W4385358041, https://openalex.org/W4312644097, https://openalex.org/W4290991295, https://openalex.org/W2964255692, https://openalex.org/W3011507876, https://openalex.org/W3008819584, https://openalex.org/W2998187693, https://openalex.org/W4282943221, https://openalex.org/W2998367975, https://openalex.org/W3175972451, https://openalex.org/W2981604716, https://openalex.org/W6750227808, https://openalex.org/W2618530766, https://openalex.org/W2746553466, https://openalex.org/W2785008862, https://openalex.org/W4221007933, https://openalex.org/W4206867326, https://openalex.org/W4298857966, https://openalex.org/W3106357768, https://openalex.org/W2548134372, https://openalex.org/W4293584584 |
| referenced_works_count | 64 |
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