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IoT-based real time intelligent routing for emergent crowd evacuation

机译:基于物联网的实时智能路由,以获得紧急人群疏散

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Purpose The purpose of this paper is to take advantage of Internet of Things (IoT) for intelligent route programming of crowd emergency evacuation in metro station. It is a novel approach to ensure the crowd safety and reduce the casualties in the emergency context. An evacuation route programming model is constructed to select a suitable evacuation route and support the emergency decision maker of metro station. Design/methodology/approach The IoT technology is employed to collect and screen information, and to construct an expert decision model to support the metro station manager to make decision. As a feasible way to solve the multiple criteria decision-making problem, an improved multi-attributive border approximation area comparison (MABAC) approach is introduced. Findings The case study indicates that the model provides valuable suggestions for evacuation route programming and offers practical support for the design of an evacuation route guidance system. Moreover, IoT plays an important role in the process of intelligent route programming of crowd emergency evacuation in metro station. A library has similar structure and crowd characteristics of a metro station, thus the intelligent route programming approach can be applied to the library crowd evacuation. Originality/value The highlights of this paper are listed as followings: the accuracy and accessibility of the metro station's real-time information are improved by integrating IoT technology with the intelligent route programming of crowd emergency evacuation. An improved MABAC approach is introduced to the expert support model. It promotes the applicability and reliability of decision making for emergency evacuation route selection in metro station. It is a novel way to combine the decision-making methods with practice.
机译:目的本文的目的是利用地铁站中人群紧急疏散的智能路线规划的东西(物联网)。这是一种新的方法,可以确保人群安全,减少紧急环境中的伤亡。建造疏散路线编程模型,以选择合适的疏散路线并支持地铁站的紧急决策者。设计/方法/方法IOT技术被用于收集和屏幕信息,并构建专家决策模型,以支持地铁站经理做出决定。作为解决多标准决策问题的可行方法,介绍了改进的多属性边界近似区域比较(MABAC)方法。结果表明案例研究表明,该模型为疏散路线编程提供了有价值的建议,并为疏散路线引导系统的设计提供了实际支持。此外,IOT在地铁站中人群紧急疏散的智能路线编程过程中发挥着重要作用。图书馆具有类似的地铁站结构和人群特征,因此智能路线编程方法可以应用于图书馆人群疏散。原创性/值本文的亮点列为以下内容:通过将IoT技术与人群紧急疏散的智能路由编程集成了地铁站的实时信息的准确性和可访问性。将改进的MABAC方法引入专家支持模型。它促进了地铁站紧急疏散路线选择决策的适用性和可靠性。它是将决策方法与实践相结合的新方法。

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