Freeway Traffic Control Using Iterative Learning Control-Based Ramp Metering and Speed Signaling

This paper presents an iterative learning control (ILC) approach for freeway density control in a ramp metering and speed regulated environment. The ILC approach is shown to improve traffic performance significantly by guaranteeing asymptotic convergence of traffic density to the desired density, despite the presence of any system uncertainties or exogenous traffic perturbations. The ILC approach also requires less prior modeling knowledge in the control system design.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Authors:
  • Hou, Zhongsheng
  • Xu, Jian-Xin
  • Zhong, Hongwei
• Publication Date: 2007-3

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 466-477
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 56
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Advanced traffic management systems; Machine learning; Macroscopic traffic flow; Ramp metering; Speed control; Traffic density; Traffic models
• Subject Areas: Highways; Operations and Traffic Management; I73: Traffic Control;

Filing Info

• Accession Number: 01044866
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: BTRIS, TRIS
• Created Date: Mar 30 2007 6:59AM