Evaluation of Disaster Response System Using Agent-Based Model With Geospatial and Medical Details

Bae, Jang Won; Shin, Kyohong; Lee, Hyun-Rok; Lee, Hyun Jin; Lee, Taesik; Kim, Chu Hyun; Chul, Won; Kim, Gi Woon; Moon, Il‐Chul

doi:10.1109/tsmc.2017.2671340

Cited by 18 publications

(8 citation statements)

References 35 publications

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“…Research Content Macroscopic Helbing et al (2001) developed a gas-kinetic traffic equation based traffic model for traffic flow analysis Boel & Mihaylova (2006) developed a compositional stochastic model for freeway traffic simulation model Thonhofer et al (2018) developed a flexible traffic model for large-scale urban traffic simulation Microscopic Hidas (2002) developed an agent-based traffic simulation model for evaluating road congestion and dynamic road guidance Klügl & Bazzan (2004) developed a simulation model for route choice decisions of individual drivers and traffic forecasts Kumar & Mitra (2006) developed a simulation model for analyzing virtual traffic situations where traffic signals malfunctioned in India Mesoscopic Taylor (2003) developed a simulation model where grouped vehicles are moving on the road network with the speed calculated by a speed density function Sun et al (2020) evaluated the potential impacts of various levels of high occupancy vehicle lane usages with commercial mesoscopic traffic simulation tools Mihăiţă et al (2019) built an simulation model describes traffic environmental changes inside the neighborhood including air pollution, traffic flow or meteorological information Ours proposed a simulation model with traffic individuals incorporating its moving behavior extracted from the real-data and elaborating on the speed-density function for its moving speed There have been a number of works about simulation-based policy evaluation in various domains, such as economics (Coester et al 2018;Yun & Moon 2020), demographics (Kim et al 2017;Singh et al 2018), and public health (Barbrook-Johnson et al 2017;Bae et al 2018). This research trend has also appeared in urban administrative studies using traffic simulations: for example, Adelt et al (2018) argued that traffic simulation could be applied to analyze the governance of complex sociotechnical systems, but their efforts ended up being a conceptual framework and a virtual small example.…”

Section: Literaturementioning

confidence: 99%

Agent-Based Model for Urban Administration: A Case Study of Bridge Construction and its Traffic Dispersion Effect

Yun

Kim

Moon

et al. 2022

JASSS

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From the viewpoint of urban administration, simulation is regarded as a policy tool that provides administrators with information about the current urban situation and enables them to verify the effectiveness of urban policies. This study proposes a traffic simulation model for a real city named Sejong in South Korea. Our proposed model employs agent-based simulation with the city-level real data, which mainly focuses on describing the movement behavior of individuals using urban traffics in the real city. By aggregating the agents' decisions and interactions during the movement, the proposed model can discover a demand for the city's transportation system. To do this, this study validated the proposed model so that the modeled traffic system was similar to the real one, and then we conducted a case study to compare and analyze the effects of traffic dispersion led by the upcoming bridge construction in the real city. The case study showed that the proposed model can provide policy evaluation on the optimal location of the bridge construction considering the city traffic flow. Furthermore, the case study presented that the agent-based modeling enables micro-level analysis on the city traffic flow to understand on the policy implications.

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Section: Literaturementioning

confidence: 99%

Agent-Based Model for Urban Administration: A Case Study of Bridge Construction and its Traffic Dispersion Effect

Yun

Kim

Moon

et al. 2022

JASSS

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“…MAS is used to describe an approach to the analysis and development of telemedicine systems [22], to manage communications in wireless sensor networks [23], the epidemiological decision support system [24], the care of seniors at home [25], decision-making for monitoring and prevention of epidemics [26], evaluation of disaster response system [27], medical sensor modules in conjunction with wireless communication technology supporting a wide range of services including mobile telemedicine, patient monitoring, emergency management and information sharing between patients and doctors or among the healthcare workers [28].…”

Section: Related Workmentioning

confidence: 99%

“…An Agent-Based Model (ABM) with Geospatial and Medical Details was used to evaluate the efficiency of disaster responders to rescue victims in a mass casualty incident situation in South Korea [27].…”

Section: Related Workmentioning

confidence: 99%

A Multiagent-Based Model for Epidemic Disease Monitoring in DR Congo

Mfumu

Mercier

Occello

et al. 2019

Biomedical Engineering Systems and Technologies

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Any infectious diseases have been reported in sub-Saharan countries over the past decade due to the inefficiency of health structures to anticipate outbreaks. In a poorly-infrastructure country such as the Democratic Republic of Congo (DRC), with inadequate health staff and laboratories, it is difficult to respond rapidly to an epidemic, especially in rural areas. As the DRC's health system has three levels (peripheral, regional and national), from the production of health data at the peripheral level to the national level that makes the decision, meantime the disease can spread to many people. Lack of communication between health centres of the same health zone and Health zones of the same Health Provincial Division does not contribute to the regional response. This article, an extended version of [1], proposes a well elaborated solution track to deal with this problem by using an agent-centric approach to study by simulation how to improve the process. A new experiment is described by arranging twentyeight health zones of Kinshasa to show how their collaboration can provide unique health data source for all stakeholders and help reducing disease propagation. It concerns also 47 health centres, 1 medical laboratory, 1 Provincial Health Division and 4 Rapid Riposte Teams. The simulation data, provided by Provincial Health Division of Kinshasa, concerned cholera outbreak from January to December 2017. The interaction between these agents demonstrated that Health Zone Agent can automatically alert his neighbours whenever he encountered a confirmed case of an outbreak. This action can reduce disease propagation as population will be provided with prevention measures. These interactions between agents have provided models to propose to the current system in order to find out the best that can help reducing decision time.Access to health care is a major concern in developing countries. The Democratic Republic of Congo ranks among the poorest countries according to its HDI 1 [2]. Despite its millions of hectares of arable land, this vast country of Central Africa is 1 Human Development Index.

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“…La modelación basada en agentes es una metodología que incorpora el comportamiento sistémico de las interacciones de los elementos que componen un sistema (Altay y Pal, 2014). De este modo, MBA se asume como una alternativa apropiada para describir el comportamiento complejo de los sistemas al considerar la integración e inter-relación de múltiples agentes modelados individualmente, y que generan un resultado global en el sistema (Krejci, 2015;Bae et al, 2018).…”

Section: Modelación Basada En Agentes (Mba)unclassified

Using a combinatorial auction-based approach for simulation of cooperative rescue operations in disaster relief

Zhu

Tang

Guo

et al. 2018

Int. J. Model. Simul. Sci. Comput.

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In practice, we experience low efficiency of search and rescue (SAR) frequently in disaster relief. Here, we will optimize the SAR through agent-based simulation. In the kind of cases described here, rescue teams are characterized by different capabilities, and the tasks often require different capabilities to complete. To this end, a combinatorial auction-based task allocation scheme is used to develop a cooperative rescue plan for the heterogeneous rescue teams. Then, we illustrate the proposed cooperative rescue plan in different scenarios with the case of landslide disaster relief. The simulation results indicate that the combinatorial auction-based cooperative rescue plan would increase victims’ relative survival probability by 13.8–16.3%, increase the ratio of survivors getting rescued by 10.7–12.7%, and decrease the average elapsed time for one site getting rescued by 19.0–26.6%. The proposed rescue plan outperforms the rescue plan based on the F-Max-Sum a little bit. The robustness analysis shows that the proposed rescue plan is relatively reliable on condition that both the search radius and scope of cooperation are larger than thresholds. Furthermore, we have investigated how the number of rescue teams influences the rescue efficiency.

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Evaluation of Disaster Response System Using Agent-Based Model With Geospatial and Medical Details

Cited by 18 publications

References 35 publications

Agent-Based Model for Urban Administration: A Case Study of Bridge Construction and its Traffic Dispersion Effect

Agent-Based Model for Urban Administration: A Case Study of Bridge Construction and its Traffic Dispersion Effect

A Multiagent-Based Model for Epidemic Disease Monitoring in DR Congo

Using a combinatorial auction-based approach for simulation of cooperative rescue operations in disaster relief

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