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A Data-Driven Control Strategy for Urban Express Ramp.

Liyan Zhang1, Min Zhang1, Jian Ma1

  • 1School of Civil Engineering, Suzhou University of Science and Technology, 1701 Binhe Road, New District, Suzhou, Jiangsu 215011, China.

Computational Intelligence and Neuroscience
|November 26, 2021
PubMed
Summary
This summary is machine-generated.

Intelligent network technology improves urban expressway traffic flow. A new cooperative control strategy for variable speed limits (VSL) and ramp metering (RM) reduces travel time by 20.59% and improves overall network efficiency.

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Area of Science:

  • Intelligent Transportation Systems
  • Traffic Engineering
  • Networked Control Systems

Background:

  • Urban expressways are critical for traffic flow but face increasing safety and congestion issues due to inadequate traffic control.
  • Intelligent network technology offers a promising solution for optimizing expressway traffic management.

Purpose of the Study:

  • To develop a data-driven ramp control model for urban expressways.
  • To implement and evaluate a cooperative control strategy integrating variable speed limits (VSL) and ramp metering (RM) within an intelligent network environment.

Main Methods:

  • Construction of a data-driven ramp control model for urban expressways.
  • Utilization of intelligent network technology for traffic information interaction.
  • Adoption of a cooperative control strategy combining VSL and RM, with mutual feedback based on an improved METANET model.
  • Simulation experiments using VISSIM secondary development for validation.

Main Results:

  • The collaborative control strategy significantly reduced vehicle travel time by 20.59%.
  • The speed difference between adjacent expressway mainline sections decreased by 34.07%.
  • Demonstrated coordinated control between the expressway mainline and on-ramps under an intelligent network environment.

Conclusions:

  • The intelligent network-based collaborative control strategy effectively alleviates expressway traffic congestion.
  • The proposed model enhances traffic pressure reduction and improves overall road network efficiency.
  • This approach provides a viable solution for optimizing urban expressway operations through intelligent control.