Dynamic Division of Control Subareas for Highway Networks Based on Improved Label Propagation Algorithm
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 9
Abstract
The aim of this work is to investigate dynamic division of highway control subareas based on improved Label Propagation Algorithm (LPA) for implementing different signal control strategies in the subareas to improve operation efficiency. A highway network is used as the studied object, where each road segment is defined as a node and the network is divided into three types of nodes: only mainline segment, an on-off-ramp and its connecting mainline, and an overpass. The system is abstracted as a topological set of nodes and their relationships. The concept of multiroad segment correlation degree with predictive function of congestion and dissipation ranges is proposed. The correlation degree model of multiroad segments based on Pearson Correlation Coefficient (PCC) and the importance degree model of road segment based on technique for order preference by similarity to ideal solution (TOPSIS) are established, respectively. Then a dynamic division method of control subareas for highway network is proposed based on improved LPA, where the label updating model based on the importance degree of road segment is established by semisupervised classification algorithm. Multiple scenarios were discussed using the highway network of Baoding City in Hebei Province of China as an example. The results show that the number of subareas is moderate, the internal state is uniform, and the calculation is simple and efficient. The control effect is remarkable especially when the vehicle distribution is uneven. Total delay time is reduced by 10.47%, 4.38%, 4.24%, 9.73%, and 2.33%, respectively, and total travel time is reduced by 12.20%, 7.15%, 7.02%, 11.85%, and 3.78%, respectively, compared with no control method, global control method, the static division method by clustering algorithm, the method by normal LPA algorithm, and the method by Newman algorithm.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
The work described in this paper was supported by the National Natural Science Foundation of China (50478088), the Science and Technology Project of Hebei Education Department in China (ZD2021028), and the Cooperation Special Project of Beijing-Tianjin-Hebei Basic Research in China (F2024202106). The authors gratefully acknowledge the editor’s comments and the reviewers of the paper who helped to clarify and improve the presentation.
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 9 • September 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Oct 20, 2023
Accepted: Apr 3, 2024
Published online: Jun 25, 2024
Published in print: Sep 1, 2024
Discussion open until: Nov 25, 2024
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