Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control Strategy Article Swipe
Ji‐Ho Park
,
Tong Liu
,
Chieh Wang
,
Andy Berres
,
Joseph Severino
,
Juliette Ugirumurera
,
Airton G Kohls
,
Hong Wang
,
Jibonananda Sanyal
,
Zhong‐Ping Jiang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1061/jtepbs.teeng-8376
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1061/jtepbs.teeng-8376
Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. Here, this study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1061/jtepbs.teeng-8376
- OA Status
- green
- Cited By
- 1
- References
- 88
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405386435
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405386435Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1061/jtepbs.teeng-8376Digital Object Identifier
- Title
-
Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control StrategyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-14Full publication date if available
- Authors
-
Ji‐Ho Park, Tong Liu, Chieh Wang, Andy Berres, Joseph Severino, Juliette Ugirumurera, Airton G Kohls, Hong Wang, Jibonananda Sanyal, Zhong‐Ping JiangList of authors in order
- Landing page
-
https://doi.org/10.1061/jtepbs.teeng-8376Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.osti.gov/biblio/2483413Direct OA link when available
- Concepts
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Regulator, SIGNAL (programming language), Control theory (sociology), Computer science, Linear-quadratic regulator, Control (management), Artificial intelligence, Biology, Biochemistry, Programming language, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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