Resilience and Vulnerability of Public Transportation Fare Systems: The Case of the City of Rio De Janeiro, Brazil

Abstract

Resilience is the ability of a system to adapt, persist, and transform as a reaction to threats, which may be external or internal to the system, while vulnerability is the state of being susceptible to harm from exposure to stresses associated with environmental and social change and from the inability to adapt. Based on a study of the threats that can affect urban mobility, we identified a gap regarding the analysis of the levels of resilience and vulnerability in the face of subsidy threats that can severely affect developing countries. This article measures the level of resilience and vulnerability due to the absence of public transport fare subsidies. For this purpose, we developed an approach based on fuzzy logic and applied it in 33 administrative regions (ARs) of the city of Rio de Janeiro, Brazil. We obtained four matrices of the levels of vulnerability and resilience of each of the regions as an origin and destination. The results show that areas nearest to the downtown region and those with high-capacity transportation available (commuter train and/or subway, systems with many transfer points) are more resilient, while a high level of vulnerability is associated with low income, negative socioeconomic indicators, and the predominance of road transportation to reach jobs. The contribution of this paper is the method applied to analyse the levels of vulnerability and resilience of public transport, which includes a threat that can cause a rupture that impacts routines and job accessibility in a region.

Transport Poverty in Chinese Cities: A Systematic Literature Review

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Physical activity among women of low socioeconomic status living with HIV in two major cities of Brazil and Mozambique: A cross-sectional comparative study

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Violence in Public Transport: An Analysis of Resilience and Vulnerability in the City of Rio de Janeiro

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“…Using fuzzy concepts, evaluators can use linguistic terms to assess indicators in a natural language expression, where each linguistic term is associated with a membership function. Then, the necessary concepts of fuzzy system modelling is described as follows (Vinodh & Devasan, 2011;Sałabun et al, 2019;Yager, 2000;Sałabun, 2014;Faizi et al, 2018), based on the work Santos et al (2020).…”