Microsimulation Study Evaluating the Benefits of Cyclic and Non-Cyclic Max-Pressure Control of Signalized Intersections

Jake Robbennolt, Rongsheng Chen, Michael Levin

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

Abstract

Max-pressure control is a decentralized method of traffic intersection control, making computations at individual intersections simple. In addition, this method of control has been proven to maximize network throughput if any traffic signal control can stabilize the demand. This paper tests max-pressure controllers in a large-scale microsimulation of the downtown Austin network using the microscopic traffic simulation package SUMO. Nine combinations of weight function and method of defining green time are studied to see how different variations on the max-pressure controller compare. It is shown that the way green time is assigned (cyclic or non-cyclic) has a larger impact on performance than the weight function used by the max-pressure controller. Based on these results a new way of assigning green time is devised. This novel controller mirrors the performance of either the cyclic or the non-cyclic controller depending on the geometry and demand. Large-scale simulation shows that this controller compares favorably with existing controllers using metrics of number of waiting vehicles and average travel time. Common problems with non-cyclic control include the higher likelihood of gridlock and the potential for very long waiting times when demand at a single intersection is asymmetric. On the other hand, the cyclic controller is required to allocate green time to every phase even if the demand is low, increasing the loss time. The novel semi-cyclic controller solves these inherent problems with the cyclic and non-cyclic controllers, making it more likely to be implemented by traffic engineers.

Original languageEnglish (US)
Title of host publicationTransportation Research Record
PublisherSage Publications Ltd
Pages303-317
Number of pages15
Volume2676
Edition12
DOIs
StatePublished - Dec 2022

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We gratefully acknowledge the support of the National Science Foundation, Award No. 1935514.

Publisher Copyright:
© SAGE Publications Ltd. All rights reserved.

Keywords

  • networks
  • optimization
  • planning and analysis
  • simulation modeling

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