Review on modeling the societal impact of infrastructure disruptions due to disasters

TL;DR

This paper reviews modeling approaches for understanding societal impacts of infrastructure disruptions caused by disasters, highlighting four main methods and discussing future research challenges.

Contribution

It provides a comprehensive classification and comparison of existing modeling approaches for societal impacts of infrastructure disruptions, guiding future research directions.

Findings

Four main modeling approaches identified: extended, empirical, agent-based, big data-driven

Comparison of approaches based on data input, impact types, and application scope

Discussion of challenges and future research directions in the field

Abstract

Infrastructure systems play a critical role in providing essential products and services for the functioning of modern society; however, they are vulnerable to disasters and their service disruptions can cause severe societal impacts. To protect infrastructure from disasters and reduce potential impacts, great achievements have been made in modeling interdependent infrastructure systems in past decades. In recent years, scholars have gradually shifted their research focus to understanding and modeling societal impacts of disruptions considering the fact that infrastructure systems are critical because of their role in societal functioning, especially under situations of modern societies. Exploring how infrastructure disruptions impair society to enhance resilient city has become a key field of study. By comprehensively reviewing relevant studies, this paper demonstrated the definition and types of societal impact of infrastructure disruptions, and summarized the modeling approaches into four types: extended infrastructure modeling approaches, empirical approaches, agent-based approaches, and big data-driven approaches. For each approach, this paper organized relevant literature in terms of modeling ideas, advantages, and disadvantages. Furthermore, the four approaches were compared according to several criteria, including the input data, types of societal impact, and application scope. Finally, this paper illustrated the challenges and future research directions in the field.