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Towards Semi-Automated Arterials: Dynamic Traffic Signal Control With Time-Dependent Variable Speed

File Language:
English


Details

  • Creators:
    Abu-Lebdeh, Ghassan
  • Corporate Creators:
    Iowa State University. Center for Transportation Research and Education
  • Subject/TRT Terms:
  • Resource Type:
    Research Paper
  • Geographical Coverage:
  • TRIS Online Accession Number:
    00793298
  • ISBN:
    0965231046
  • Corporate Publisher:
    Iowa State University
  • Abstract:
    This paper presents a framework and demonstrates the feasibility of an advanced concept for traffic control on congested urban arterials. The main idea of semi-automated arterials is to utilize a dynamically optimized time-dependent variable speed as an additional signal control parameter. In a semi-automated arterial, speeds would be automatically optimized and set by a central computer. They would change between links and over time in response to changing traffic conditions. Drivers would follow the optimized speed as they enter a link. Once an optimal speed has been set for a link, it remains constant until the control cycle ends. Link speeds would be updated only at the end of every control cycle. The control cycle may change in length as system conditions evolve. The new control concept was tested on a congested arterial with multiple links. The arterial system was modeled as a discrete event time varying dynamical system with a control period spanning several cycles. System throughput was maximized subject to such critical operational measures as intersection blockage, queue spillbacks, and other relevant traffic operation measures. Genetic Algorithms (GAs) were used as an optimization tool. Results show that the semi-automated arterial concept will significantly improve traffic flow. The new control concept is suitable for on-line implementation in an Intelligent Transportation System (ITS) setting.
  • Format:
    PDF
  • Collection(s):
  • Main Document Checksum:
    urn:sha256:a2fa05c6ca6662771892b6729db31fb19f22fecc42ba353214bdc5ee290405ba
  • Download URL:
  • File Type:
    Filetype[PDF - 66.99 KB ]
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