Regional Boundary Control of Traffic Network Based on MFD and FR-PID
In recent years, urban traffic congestion has become more serious and the capacity of roads has declined, resulting in frequent traffic accidents. In order to effectively alleviate the traffic congestion of the regional road network, aiming at the problem of lack of accurate OD data of the road network, a regional boundary control method of the traffic network based on fuzzy RBF neural network PID (FR-PID) is proposed by combining the theory of macroscopic fundamental diagram (MFD). Firstly, based on the traffic survey, the simulation model of the study area is built, and the basic data such as the traffic flow and the time occupation rate of each road section are obtained. Secondly, the simulation data are used to test the existence of MFD in the road network, and the controlled area is defined. Then, the vehicle change model of the road network area is established. Then, in view of the problem of poor adaptive ability of traditional PID control, the FR-PID control structure is designed. Finally, an example is verified by VISSIM software. In the simulation, different control methods are used for comparison and verification, and the simulation results are analyzed. The results show that the control effect of the proposed method is better than that of the traditional method, and the regional average accumulative vehicle number, regional average completed volume, regional accumulative delays, and total vehicle travel time are optimized by 28.21%, 41.19%, 27.06%, and 32.73%, respectively. The research results can provide reference for the management of urban congestion, thereby reducing the number of traffic accidents and improving urban traffic safety.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
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Supplemental Notes:
- © 2021 Xin Yang et al.
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Authors:
- Yang, Xin
- Chen, Juncheng
- Yan, Mantun
- He, Zhao
- Qin, Ziyan
- Zhao, Jiandong
- Publication Date: 2021-9
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: Article ID 9730813
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Serial:
- Journal of Advanced Transportation
- Volume: 2021
- Publisher: John Wiley & Sons, Incorporated
- ISSN: 0197-6729
- EISSN: 2042-3195
- Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Networks; Neural networks; Traffic congestion; Traffic control
- Subject Areas: Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01781770
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 20 2021 2:52PM