Traffic signal optimization for partially observable traffic system and low penetration rate of connected vehicles
Two crucial needs for a partially observable traffic control system are observability and controllability. The authors propose a step-by-step method for traffic signal optimization, using connected vehicle (CV) data. In step 1, for traffic volume estimation, a Bayesian deduction method based on low-penetration CV data is created. In step 2, to simultaneously optimize the flexible lane settings and signal timings, (which form the prior information for step 3), an offline signal optimization model is constructed. In step 3, based on real-time traffic data, an online deep Q-learning (DRQN) signal optimization adjusts signal settings. The authors’ study combines innovative traffic state estimation and traffic signal control in an integrated process, contributing to better understanding of traffic control in CV traffic while constituting a solid basis for traffic control strategies in future.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/10939687
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Supplemental Notes:
- © 2022 Computer-Aided Civil and Infrastructure Engineering.
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Authors:
- Zhang, Zhao
- Guo, Mengdi
- Fu, Daocheng
- Mo, Lei
- Zhang, Siyao
- Publication Date: 2022-12
Language
- English
Media Info
- Media Type: Web
- Features: Appendices; Figures; References; Tables;
- Pagination: pp 2070-2092
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Serial:
- Computer-Aided Civil and Infrastructure Engineering
- Volume: 37
- Issue Number: 15
- Publisher: Blackwell Publishing
- ISSN: 1093-9687
- Serial URL: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-8667
Subject/Index Terms
- TRT Terms: Connected vehicles; Lane distribution; Optimization; Signalized intersections; Traffic data; Traffic signal timing
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01886444
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 28 2023 4:29PM