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Coordinated ramp signal optimization framework based on time series flux-correlation analysis.

Zhi Liu1, Wendi Shu1, Guojiang Shen1

  • 1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China.

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Summary

This study introduces a new method for optimizing ramp signals on urban expressways by considering real-time traffic flow. The approach reduces mainline congestion and improves traffic efficiency.

Keywords:
Correlation analysisGRU neural networkRamp signal optimization

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Area of Science:

  • Traffic Engineering
  • Transportation Science
  • Urban Planning

Background:

  • Urban expressways are crucial for managing traffic congestion.
  • Current ramp signal optimization relies on static distances, ignoring dynamic traffic flow and causing delays.

Purpose of the Study:

  • To develop a coordinated ramp signal optimization framework using mainline traffic states.
  • To enhance the efficiency of urban expressway traffic flow.

Main Methods:

  • Developed a framework based on mainline traffic states.
  • Utilized traffic flow-series flux-correlation analysis and a multifactorial matrix.
  • Employed GRU neural networks for traffic flow prediction and gray correlation analysis for factor weighting.

Main Results:

  • The proposed method effectively reduces mainline bottleneck density.
  • Demonstrated improved mainline traffic efficiency under various demand conditions.
  • Validated through simulations using the Simulation of Urban Mobility platform.

Conclusions:

  • The novel framework offers a dynamic and real-time approach to ramp signal optimization.
  • This method addresses the limitations of static-based systems, improving overall expressway performance.