{"title":"N-MP: A network-state-based Max Pressure algorithm incorporating regional perimeter control","authors":"Hao Liu , Vikash V. Gayah","doi":"10.1016/j.trc.2024.104725","DOIUrl":null,"url":null,"abstract":"<div><div>The Max Pressure (MP) framework has been shown to be an effective real-time decentralized traffic signal control algorithm. However, despite its superior performance and desirable features – such as the maximum stability property – it may still suffer from deterioration in network mobility due to the rise of congestion within specific regions of an urban traffic network. To address this drawback and further improve the performance of MP control in urban networks, this paper proposes a novel MP algorithm that incorporates regional traffic states into the MP framework. The proposed model – called N-MP – simultaneously integrates perimeter metering control at the boundary of regions of a network to be protected with traditional local intersection control. The proposed model is the first to incorporate perimeter metering control fully within a decentralized signal control environment and inherits the maximum stability property. In addition, it does not require extra traffic state measurements compared to the original MP algorithms, beyond a measure of congestion within the protected region of the network. Microscopic traffic simulation results demonstrate that the proposed model can outperform two baseline perimeter control models – Bang–Bang control and feedback gating – under various traffic conditions. More interestingly, this superiority is maintained in both fully and partially connected environments.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"168 ","pages":"Article 104725"},"PeriodicalIF":7.6000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part C-Emerging Technologies","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968090X24002468","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The Max Pressure (MP) framework has been shown to be an effective real-time decentralized traffic signal control algorithm. However, despite its superior performance and desirable features – such as the maximum stability property – it may still suffer from deterioration in network mobility due to the rise of congestion within specific regions of an urban traffic network. To address this drawback and further improve the performance of MP control in urban networks, this paper proposes a novel MP algorithm that incorporates regional traffic states into the MP framework. The proposed model – called N-MP – simultaneously integrates perimeter metering control at the boundary of regions of a network to be protected with traditional local intersection control. The proposed model is the first to incorporate perimeter metering control fully within a decentralized signal control environment and inherits the maximum stability property. In addition, it does not require extra traffic state measurements compared to the original MP algorithms, beyond a measure of congestion within the protected region of the network. Microscopic traffic simulation results demonstrate that the proposed model can outperform two baseline perimeter control models – Bang–Bang control and feedback gating – under various traffic conditions. More interestingly, this superiority is maintained in both fully and partially connected environments.
期刊介绍:
Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.