Network-Wide Traffic State Estimation and Rolling Horizon-Based Signal Control Optimization in a Connected Vehicle Environment

This paper presents an innovative method to adaptively optimize traffic signal plans based on the estimation of traffic situation achieved from the information of various penetration rates of Connected Vehicles (CVs). The network-wide signal control problem is formulated as a linear optimization problem. Moreover, the authors develop a Kalman filter (KF) and Neural Network (NN) algorithms to predict and update the traffic situation under mixed non-connected and connected vehicles environment. To capture the dynamic of the traffic flow, they employ the cell transmission model synched with the Vissim traffic simulator. The methodology is tested using a challenging network of six intersections. They test their model for various Penetration Rates (PR) of the CV to provide a comparative analysis. The performance of the method is also compared with a conventional actuated-coordinated traffic signal plan. The results show that with a bare minimum PR (say more than 30%), their proposed methodology outperforms the actuated traffic signal plan. (note that the minimum PR is subject to further ongoing research in the literature, to the extent that lower PRs might be plausible). Though a 100% PR is highly desirable, the authors' method can fetch the maximum benefit just by 60% PR.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2021.3059705
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Emami, Azadeh
  • Sarvi, Majid
  • Bagloee, Saeed Asadi
• Publication Date: 2022-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 5840-5858
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 6
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Connected vehicles; Neural networks; Optimization; Traffic characteristics; Traffic flow; Traffic signal control systems; Vehicle mix
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01855760
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 24 2022 3:02PM