{"title":"Unveiling network capacity potential with imminent supply information part II: Backpressure-based validation","authors":"Dianchao Lin, Li Li","doi":"10.1016/j.trb.2025.103153","DOIUrl":null,"url":null,"abstract":"The capacity region (CR) is a key index to characterize a dynamic processing system’s ability to handle incoming demands. It is a multidimensional space when the system has multiple origin–destination pairs where their service rates interact. An urban traffic network is such a system. Traffic congestion appears when its demand approaches or exceeds the upper frontier of its CR. Part I of this study theoretically proved that (1) accurate I-SFR information of additional conflicting movements can enlarge the CR, and (2) improving the I-SFR prediction accuracy of observed movements can expand the CR. However, such expansion has not been validated through experiments. Part II of this study thus focuses on validating the theoretical findings in Part I. We use a real-time traffic control policy, named BackPressure (BP) control, to act as a ruler to measure the size of CR. We first prove that BP policy with partial I-SFR information can stabilize the network within the corresponding CR. Then we design various calibrated simulation experiments to check the validity of the two findings in Part I. Specifically, we use reserve demand, which represents the distance between a given demand and the frontier of CR, as a direct index to reflect the size of CR, and use delay as an indirect index to reflect the changes in CR. Simulation results confirm the theories in Part I.","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"97 1","pages":""},"PeriodicalIF":5.8000,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part B-Methodological","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.trb.2025.103153","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
The capacity region (CR) is a key index to characterize a dynamic processing system’s ability to handle incoming demands. It is a multidimensional space when the system has multiple origin–destination pairs where their service rates interact. An urban traffic network is such a system. Traffic congestion appears when its demand approaches or exceeds the upper frontier of its CR. Part I of this study theoretically proved that (1) accurate I-SFR information of additional conflicting movements can enlarge the CR, and (2) improving the I-SFR prediction accuracy of observed movements can expand the CR. However, such expansion has not been validated through experiments. Part II of this study thus focuses on validating the theoretical findings in Part I. We use a real-time traffic control policy, named BackPressure (BP) control, to act as a ruler to measure the size of CR. We first prove that BP policy with partial I-SFR information can stabilize the network within the corresponding CR. Then we design various calibrated simulation experiments to check the validity of the two findings in Part I. Specifically, we use reserve demand, which represents the distance between a given demand and the frontier of CR, as a direct index to reflect the size of CR, and use delay as an indirect index to reflect the changes in CR. Simulation results confirm the theories in Part I.
期刊介绍:
Transportation Research: Part B publishes papers on all methodological aspects of the subject, particularly those that require mathematical analysis. The general theme of the journal is the development and solution of problems that are adequately motivated to deal with important aspects of the design and/or analysis of transportation systems. Areas covered include: traffic flow; design and analysis of transportation networks; control and scheduling; optimization; queuing theory; logistics; supply chains; development and application of statistical, econometric and mathematical models to address transportation problems; cost models; pricing and/or investment; traveler or shipper behavior; cost-benefit methodologies.