Optimization of Consecutive On-Ramp Control for Urban Freeways: An Application of the Store-and-Forward Approach
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 4
Abstract
Highway congestion is a common problem worldwide, and different management strategies have been proposed and implemented. In Taiwan, commuter trips in urban areas tend to use freeways for daily trips. One particular problem in Taiwan is consecutive on-ramps within metropolitan areas, such as Taipei, Taichung, and Kaohsiung. Three to four on-ramps bring more vehicles to the freeways, and coordinating with an integrated ramp control strategy is critical to avoiding congestion in metropolitan areas. Based on the concept of the store-and-forward method, this study proposes a linear optimal control model to optimize integrated on-ramp control strategies. The store-and-forward method is chosen because freeway congestion is usually observed in these areas, and freeway efficiency depends on avoiding queue length. The developed mathematical program can be solved efficiently through mathematical optimization packages. The objective of the formulation is to minimize the total queue length within considered areas. In the Taipei metropolitan area, numerical experiments were conducted based on three consecutive on-ramps, including Yuanshan, Taipei, and Sanchun ramps. Several scenarios were designed to illustrate the possible benefits of the proposed model.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This paper is based on the research projects supported by the Ministry of Science and Technology, Taiwan, ROC. The authors are solely responsible for the contents of this paper.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jun 17, 2021
Accepted: Oct 6, 2022
Published online: Jan 18, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 18, 2023
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