Intelligent Evacuation Management Systems

Ibrahim, Azhar Mohd; Venkat, Ibrahim; Subramanian, K. G.; Khader, Ahamad Tajudin; Wilde, Philippe De

doi:10.1145/2842630

Cited by 68 publications

(42 citation statements)

References 84 publications

(74 reference statements)

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“…We addressed three known challenges: (1) the ambiguity of localization procedure due to noisy RSS values, (2) the MAC address randomization when a device is in a probing mode, and (3) the irregularity of the packet interarrivial times. We used probabilistic models to address (1) and (2) and a memorybased model to address (3). We showed formally that the error of our estimation tends to zero as the crowd size increases (which is essential for enabling disaster prevention), even in case when the locations of visitors are correlated as in groups of friends.…”

Section: Discussionmentioning

confidence: 98%

“…In this paper we discuss estimating crowd density to be able to detect critical density and prevent crowd disasters. We note that planning optimal evacuation and navigation of the crowd are separate research challenges and we refer the reader to [3] for a recent overview. Concretely in our case the crowd can be navigated using the large TV screens already present at the stadium, or apps that use the built-in compasses of the smartphones.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, GPS is not so suitable for indoor localization. (For more information on crowd monitoring services, we refer the reader to [3]. ).…”

mentioning

confidence: 99%

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Detecting high indoor crowd density with Wi-Fi localization: a statistical mechanics approach

et al. 2019

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Crowd disasters have taken many human lives. The Love Parade disaster in Duisburg, 2010, the Ellis Park Stadium disaster in Johannesburg, 2001, the PhilSports Stadium stampede in Manila, 2006, are just a few examples. One of the major factors contributing to crowd disasters are critically dense spots [1-3], which are difficult to detect due to lack of macroscopic overview of the crowd [1]. In this paper we address the problem of estimating the crowd density distribution in situations such as indoor dance events, to enable prevention of crowd disasters. A lot of research on estimating crowd density concerns processing video records from security cameras [4, 5]. However, this approach does not suffice to detect critically raised crowd density. Firstly, as mentioned before, it is difficult to obtain macroscopic overview of the crowd. Secondly, the lighting conditions at a concert might not be sufficient for video-based crowd analysis. Finally, the error of counting people increases with the increase of the actual crowd density [6] due to the so-called occlusion effects. Another way to monitor the crowd density is by using RFID technology [3]. Each participant is asked to wear a tag, and RFID readers are distributed across the venue. This approach, however, requires participation from the crowd and deployment costs.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

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Detecting high indoor crowd density with Wi-Fi localization: a statistical mechanics approach

et al. 2019

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“…Usually, the control functionality of I-CPS is extended with data analytics functionality, which is based on multiplexed sensor nodes and pervasive sensor networks, and information modality transformers and message generators. As reported in the literature, typical examples are distributed tourist information systems (Osborn & Hinze, 2014), context-aware navigation systems (Saeedi et al, 2014), healthcare recommendation systems (Shojanoori et al, 2012), patient context monitoring systems (Kataria et al, 2008), and evacuation management systems (Ibrahim et al, 2016). The servicing activities of I-CPSs include various messaging functions such as: (i) selecting informing modality, (ii) constructing personalized messages, and (iii) distributing messages to the stakeholders.…”

Section: Introductionmentioning

confidence: 99%

Personalized Messaging Based on Dynamic Context Assessment: Application in An Informing Cyber-Physical System

Horváth

Rusák

2020

JID

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Hazard-intense applications of cyber-physical systems (CPSs) such as the evacuation of a building on fire requires optimal management of stakeholders. Personalized message generation and informing of stakeholders based on real-time assessment of the dynamic context of stakeholders is the research and engineering challenge addressed by this paper. Personalized multi-message construction mechanism (MCM) that is enabled by dynamic context modeling, inferring and reasoning is proposed. Dynamic personal context was defined as the total of space-and time-varying situations that are relevant to a stakeholder. The basis of generating messages is a quantitative evaluation of the implications of the relevant situations with regards to the target stakeholders. The concept of impact indicator was used to represent the implications of situations and a personal danger level indicator was used to choose a proper message template for message construction. The algorithms included in the MCM were validated in a (simulated) indoor fire evacuation guiding application. Test people were involved in the practical evaluation of the quality of the generated messages. The conclusion is that the proposed MCM provides more sufficient information about personal context and expected actions than the messages constructed based on static context information.

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“…Evacuation simulation of the movement and behavior of a crowd during egress could reduce the possibility of crowd disaster [21,22]. It is an undeniable fact that behavior of a crowd is intrinsic and could be influenced by external factors such as clogging, counter flow, narrow path and congestion.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in a spatial evacuation model

Ibrahim

Venkat

Wilde

2017

Physica A: Statistical Mechanics and its Applications

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Intelligent Evacuation Management Systems

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 68 publications

References 84 publications

Detecting high indoor crowd density with Wi-Fi localization: a statistical mechanics approach

Detecting high indoor crowd density with Wi-Fi localization: a statistical mechanics approach

Personalized Messaging Based on Dynamic Context Assessment: Application in An Informing Cyber-Physical System

Uncertainty in a spatial evacuation model

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