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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.
Peerj. Computer Science
|April 9, 2021
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.
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.
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