Early-warning analysis of crowd stampede in metro station commercial area based on internet of things

• Published in: Multimedia tools and applications Vol. 78; no. 21; pp. 30141 - 30157
• Main Authors: Xie, Kefan, Mei, Yanlan, Gui, Ping, Liu, Yang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2019; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Data Structures and Information Theory; Electronic commerce; Emergency management; Emergency warning programs; Internet of Things; Multimedia Information Systems; Site planning; Special Purpose and Application-Based Systems; Subways; Internet of things; Crowd stampede; Early-warning; Metro Station; Intelligent computing
• ISSN: 1380-7501; 1573-7721; 1573-7721
• DOI: 10.1007/s11042-018-6982-5

Crowd stampede has attracted significant attention of emergency management researchers in recent years. Early-warning of crowd stampede in metro station commercial area is discussed in this paper under the context of Internet of Things (IoT). Metro station commercial area is one of the entity carriers of E-commerce. IOT is a new concept of realizing intelligent sense, monitoring, tracking and management, which can be used in early-warning analysis of crowd stampede in metro station. Stampede risk early-warning in commercial area plays an important role in ensuring the operation of e-commerce online. Firstly, the laws and characteristics of the crowd movement in the commercial area of metro station are studied, which include the laeuna effect, block effect and aggravation effect. Secondly, the early-warning paradigm is constructed from four dimensions, ie. function, modules, principle and process. And then, under the IOT environment, the AHPsort II is applied to integrate the early-warning information and classify the stampede risk level. Finally, the paper takes the commercial area of Wuhan A metro station as an example to verify the practicability and effectiveness of the AHPsort II application to early-warning of crowd stampede in metro station commercial area.