Human-centered modeling framework of multiple interdependency in urban systems for simulation of post-disaster recovery processes
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This paper presents a human-centered modeling framework of urban systems to capture various types of interdependency underlying urban sociotechnical and socioeconomic systems. The proposed framework consists of three major subsystems: civil life, manufacturing/service industry, and lifeline infrastructure. This framework classifies nine different types of interdependencies existing within and between these three subsystems. This paper also presents a computer simulation of the post-disaster recovery process of urban systems considering various interdependencies captured by the modeling framework. We adopt an agent-based model incorporating a network model for implementing the three subsystems as well as the various types of interdependencies. A sensitivity analysis was conducted based on the R4 framework of disaster resilience to verify and validate the simulation model. The simulation results were generally consistent with the predictions made by the R4 framework, suggesting that the model was implemented properly and can capture the multiple interdependencies behind the urban systems.
KeywordsDisaster resilience Critical infrastructure Interdependency Agent-based model Network model
This study was partly supported by Research Institute of Science and Technology for Society (RISTEX), Japan Science and Technology Agency (JST).
Compliance with ethical standards
Human and/or animals rights
This study is not involving human participants and/or animals.
Conflict of interest
The authors declare that they have no conflict of interest.
- Central Disaster Prevention Council (2013) Disaster preparedness for Nankai trough earthquake. http://www.bousai.go.jp/jishin/nankai/taisaku_wg/pdf/20130528_honbun.pdf. Accessed 26 July 2018 (in Japanese)
- Dundenhoeffer DD, Permann MR, Boring RL, 2006, Decision consequence in complex environments: visualizing decision impact. In: Proc. Decision Consequence in Complex Environments, pp 211–218Google Scholar
- Furuta K, Kanno T, 2013, Issues in service system resilience. In: Proc. 43rd Annual IEEE/IFIP Conf. Dependable Systems and Networks WorkshopGoogle Scholar
- Hayashi H, Kameda H, Nojima N (1994) Troubles people have to suffer when lifelines fail to function after earthquake. In: Proceedings from the Fifth US-Japan Workshop on earthquake resistant design of lifeline facilities and countermeasures against soil liquefaction, pp 593–599Google Scholar
- Hollnagel E (2011) RAG—the resilience analysis grid. In: Hollnagel E, Pariès J, Woods DD, Wreathall J (eds) Resilience engineering in practice. A Guidebook. Ashgate, Surrey, EnglandGoogle Scholar
- Hollnagel D, Woods DD, Leveson N (2006) Resilience engineering, concepts and precepts. Ashgate, Hampshire, EnglandGoogle Scholar
- Hollnagel E, Nemeth CP, Dekker S (2008) Resilience engineering perspectives. Ashgate, Hampshire, EnglandGoogle Scholar
- Johnson J, Wood AM (2017) Integrating positive and clinical psychology: viewing human functioning as continua from positive to negative can benefit clinical assessment. Interv Underst Resil 41:335–349Google Scholar
- Jovanovic AS, Schmid N, Klimek P (2016) Use of indicators for assessing resilience of smart critical infrastructures. In: Linkov I, Florin MV (eds) IRGC resource guide on resilience. IRGC, LausanneGoogle Scholar
- Kanno T, Suzuki T, Yoshida Y, Furuta K, 2016, Simulation of post-disaster recovery for building a resilient Tokyo. In: Proc. 6th. Int’l Conf. Building Resilience, Paper No.14Google Scholar
- Kasthurirangan G, Srinivas P (eds) (2010) Sustainable and resilient critical infrastructure systems. Springer, Berlin, HeidelbergGoogle Scholar
- Krackhardt D, Carley KM (1998) A PCANS model of structure in organizations. In: Proc. Int’l Symp. Command and control research and technology, pp 113–119Google Scholar
- Maslow A (1954) Motivation and personality. Harper & Brothers, New YorkGoogle Scholar
- Multidisciplinary Center for Earthquake Engineering Research (2018) http://www.buffalo.edu/mceer.html. Accessed 14 June 2018
- National Academies (2012) Disaster resilience. National Academies Press, Washington DCGoogle Scholar
- Reiner M, McElvaney K (2017) Foundational infrastructure framework for city resilience. Sustain Resil Infrastruct 2(1–7):1–7Google Scholar
- Resilience (2018) The Oxford English Dictionary (OED Online). http://www.oed.com/view/Entry/163619?redirectedFrom=resilience#eid. Accessed 20 June 2018
- Tierney K, Bruneau M (2007) Conceptualizing and measuring resilience: a key to disaster loss reduction. TR News 250:14–15Google Scholar
- Togawa T, Mimuro A, Kato H, Hayashi Y, Nishino S, Takano T, 2011, Evaluation post-disaster reconstruction and improvement management from QOL standards in disasters In: Proc. Joint Intl Symp. On social management systems, pp 362–369Google Scholar
- Wei D, Ji K (2010) Resilient industrial control system (RICS): concepts, formulation, metrics and insights. In: Proc. Int’l Symp. on Resilient Control Systems, pp 15–22Google Scholar