Disaster Evacuation Assistance System Based on Multi-agent Cooperation

Iizuka, Yasuki; Iizuka, Kayo

doi:10.1109/hicss.2015.30

Cited by 9 publications

(13 citation statements)

References 21 publications

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“…The crowd constantly obtains information from peers and surrounding environments, such as crowd movement trends, the degree of risk, and the crowd density of the neighborhood. It has been demonstrated that evacuation time can be reduced by 10%-30% when evacuated pedestrians receive evacuation guidance [65]. Since agents obtain information from peers, they may learn some way to avoid risk and increase the information embodied in the loss of energy when wading the waterlogging point.…”

Section: Mas Modelingmentioning

confidence: 99%

MAS-Based Evacuation Simulation of an Urban Community during an Urban Rainstorm Disaster in China

et al. 2020

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The frequent occurrence of urban waterlogging constantly affects cities’ stability, bringing about a lot of economic losses and casualties. Coupled with the deficient rescue activities, waterlogging often exacerbates the impact of urban rainstorm disasters. By setting up a diverse distribution of shelters and various types of pedestrians, the evacuation route choice of pedestrians in an urban rainstorm disaster is simulated and analyzed through multi-agent system simulation. Then, clustering analysis is applied to discover population characteristics in different survival scenarios. The simulations for sustainable rescue after pedestrians reach the shelters are also carried out. It was found that the pedestrians’ herd mentality and the distribution of shelters have a significant impact on the success rate of post-disaster evacuation. The results could help pedestrians to make decisions in the evacuation. The wide scope of the shelters’ allocation facilitates the effect of disaster relief.

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Section: Mas Modelingmentioning

confidence: 99%

MAS-Based Evacuation Simulation of an Urban Community during an Urban Rainstorm Disaster in China

et al. 2020

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“…It was found that different levels of spatial resolution or aggregation also require a simplification of the energy networks. Network reduction is currently an important field for modellers of large-scale transmission power grids [62,75,77,145,146]. This is also an important consideration when modelling MES at a distribution level.…”

Section: Time and Spacementioning

confidence: 99%

Grid-based multi-energy systems—modelling, assessment, open source modelling frameworks and challenges

2018

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Background: The transition to a sustainable future challenges the current energy grids with the integration of variable, distributed renewable energy sources. On a technical level, multi-energy systems may provide the necessary flexibility to minimise the gap between demand and supply. Suitable methods and tools are necessary to derive relevant results and to support a transition to renewable energy sources. While several, dedicated tools to model grids and infrastructure of single-energy carriers exist, there are no tools capable of modelling multi-energy systems in detail. Thus, this paper presents the necessary aspects to consider when modelling grid-based multi-energy systems, presents three open source frameworks for modelling grid-based energy systems and points out the major challenges. Methodology: The current main aspects and challenges for modelling grid-based energy systems are derived from a literature review. Three open source multi-energy modelling frameworks (Calliope, oemof, urbs) are presented, and the extent to which they consider these aspects and how they tackle challenges is analysed. Grid-based MES modelling: We identified five general energy system modelling aspects (modelling scope, model formulation, spatial coverage, time horizon, data) and three aspects specific to modelling energy grids (level of detail, spatial resolution, temporal resolution). While the specific aspects mainly influence the representation of the technical parts of the energy system and the computational effort, the general aspects primarily relate to the system boundaries and scope of the model. For the evaluation of the modelling results, we identified several assessment criteria, including economic, energetic, exergetic and reliability. Each of the studied open source modelling frameworks provides generic capabilities to model energy converters, and the electricity, gas and district heat networks. However, the general and specific aspects present respective challenges. Relating to the general aspects, complexity of model formulation increases when including additional boundary conditions. The accuracy of the results is also dependent on data quality. Temporal and spatial resolutions are the major specific challenges for modelling the energy infrastructure. Conclusions: There is still a broad field of opportunities for researchers to contribute to grid-based energy system modelling. This encompasses especially the consideration of short-and long-term dynamics of renewable energy sources in planning models.

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“…However, the system need to extend by incorporating more number of parameters and the relevant factors of humanitarian evacuation to address the uncertain factors in emergency. Y. Iizuka et al [11] presents a system that supports people being evacuated effectively from dangerous situations by using Multi-Agent cooperation. The system evaluates the location of evacuees by using mobile devices and performing distributed computing.…”

Section: Related Workmentioning

confidence: 99%

“…According to αij, βj and σj, the sum of forces (included attraction force, repulsion force, relationship attraction force) in the x-axis is Fsumx and in the yaxis is Fsumy can be calculated respectively. Assume D 2 =(Fsumx, Fsumy) as a point of as a point of the rectangular coordinate system xoy, U(Xj) and F(Xj) are calculated fur- ther with (11) and (13), and then ηj 2 is calculated with (10). Determine whether ε=|σj -ηj 2 | (i.e.…”

Section: Relationship Attraction Forcementioning

confidence: 99%

“…Many representative emergency evacuation planning systems have been designed and built at the same time. For example, M. U. S. Khan et al [10] presented an evacuation system based on Macroserv for emergency, Y. Iizuka et al [11] presented a multi-agent-based cooperative emergency evacuation system, P. G. Raj et al [12] presented a population evacuation system based on distributed mobile sensors, K. Yu et al [13] presented an intelligent evacuation guidance system based on image recognition, and F. Zhu et al [14] presented a parallel public transport evacuation system based on the Artificial societies, Computational experiments, and Parallel execution (ACP) approach. EvacSys [15] is a typical evacuation system based on Cloud computing.…”

Section: Introductionmentioning

confidence: 99%

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CLOTHO: A Large-Scale Internet of Things-Based Crowd Evacuation Planning System for Disaster Management

Zhang

Sotiriadis

et al. 2018

IEEE Internet Things J.

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In recent years, different kinds of natural hazards or man-made disasters happened that were diversified and difficult to control with heavy casualties. In this work, we focus on the rapid and systematic evacuation of large-scale densities of people after disasters to reduce loss in an effective manner. The optimal evacuation planning is a key challenge and becomes a hotspot of research and development. We design our system based on an Internet of Things (IoT) scenario that utilizes a mobile Cloud computing platform in order to develop the Crowd Lives Oriented Track and Help Optimizition system (CLOTHO). CLOTHO is an evacuation planning system for large-scale densities of people in disasters. It includes the mobile terminal (IoT side) for data collection and the Cloud backend system for storage and analytics. We build our solution upon a typical IoT/fog disaster management scenario and we propose an IoT application based on an evacuation planning algorithm that uses the Artificial Potential Field (APF), which is the core of CLOTHO. APF is conceptualized as an IoT service, and can determine the direction of evacuation automatically according to the gradient direction of the potential field, suitable for rapid evacuation of large population. People are usually in panic, which easily causes the chaos of evacuation and brings secondary disasters. Based on APF, we propose an evacuation planning algorithm names as Artificial Potential Field with Relationship Attraction (APF-RA). APF-RA guides the evacuees with relationship to move to the same shelter as much as possible, to calm evacuees and realize a more humanitarian evacuation. The experimental results show that CLOTHO (using APF and APF-RA) can effectively improve convergence rate, shorten the evacuation route length and evacuation time, and make the remaining capacity of the surrounding shelters well balanced.

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Disaster Evacuation Assistance System Based on Multi-agent Cooperation

Cited by 9 publications

References 21 publications

MAS-Based Evacuation Simulation of an Urban Community during an Urban Rainstorm Disaster in China

MAS-Based Evacuation Simulation of an Urban Community during an Urban Rainstorm Disaster in China

Grid-based multi-energy systems—modelling, assessment, open source modelling frameworks and challenges

CLOTHO: A Large-Scale Internet of Things-Based Crowd Evacuation Planning System for Disaster Management

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