Xiaoxia Yang , Baolong Shi , Guoqing Zhang , Yongxing Li
{"title":"Optimization modeling of automatic crowd regulation at bottlenecks of subway system: A model predictive control approach","authors":"Xiaoxia Yang , Baolong Shi , Guoqing Zhang , Yongxing Li","doi":"10.1016/j.physleta.2024.130180","DOIUrl":null,"url":null,"abstract":"<div><div>Optimizing crowd regulation through the setting of diversion railings at the bottleneck of subway stations is a precise and effective strategy to improve the traffic efficiency and safety of high-density crowds. The existing work mainly focuses on setting fixed railings based on management experiences, which lacks dynamic regulation strategies in the passage process. To address this issue, an automatic regulation optimization method for crowd under railings is provided based on the model predictive control. System identification methods are used to model the obtained flow data at bottlenecks based on the MassMotion, and a model predictive control system for railings, therefore, is constructed, with the length of the railing as the control input and the crowd density as the controlled object. Regulation commands have characteristics of simplicity and clarity, which can effectively intervene in the traffic state of high-density crowds, striking an optimal balance between safety and efficiency.</div></div>","PeriodicalId":20172,"journal":{"name":"Physics Letters A","volume":"531 ","pages":"Article 130180"},"PeriodicalIF":2.3000,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics Letters A","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0375960124008740","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Optimizing crowd regulation through the setting of diversion railings at the bottleneck of subway stations is a precise and effective strategy to improve the traffic efficiency and safety of high-density crowds. The existing work mainly focuses on setting fixed railings based on management experiences, which lacks dynamic regulation strategies in the passage process. To address this issue, an automatic regulation optimization method for crowd under railings is provided based on the model predictive control. System identification methods are used to model the obtained flow data at bottlenecks based on the MassMotion, and a model predictive control system for railings, therefore, is constructed, with the length of the railing as the control input and the crowd density as the controlled object. Regulation commands have characteristics of simplicity and clarity, which can effectively intervene in the traffic state of high-density crowds, striking an optimal balance between safety and efficiency.
期刊介绍:
Physics Letters A offers an exciting publication outlet for novel and frontier physics. It encourages the submission of new research on: condensed matter physics, theoretical physics, nonlinear science, statistical physics, mathematical and computational physics, general and cross-disciplinary physics (including foundations), atomic, molecular and cluster physics, plasma and fluid physics, optical physics, biological physics and nanoscience. No articles on High Energy and Nuclear Physics are published in Physics Letters A. The journal''s high standard and wide dissemination ensures a broad readership amongst the physics community. Rapid publication times and flexible length restrictions give Physics Letters A the edge over other journals in the field.