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.

Highlights

  • Policies and strategies should aim to optimise both the u-rbanisation process and urban functions and infrastructure in order to achieve sustainable development, maximise urbanisation’s benefits, and reduce urbanisation’s negative impacts [1]

  • This article examines the following questions regarding this problem: What would the most vulnerable areas be if the fare subsidy ceased to exist? What are the most resilient areas? If an area is vulnerable, does this mean it is not resilient? Do regions with mass transit have better resilience indexes? To answer these questions, here we propose a method to measure the level of resilience and vulnerability based on fuzzy logic and apply it to a real situation through a case study of the subsidy, of fare integration, for public transportation in the city of Rio de Janeiro, Brazil, from the standpoint of job access

  • Resilience refers to the capacity of a system or its components, in time and space, to maintain or quickly return to the desired functions when faced with a threat, to adapt to changes or to undergo rapid transformations [13,16], and/or to maintain the level of services offered through adequate conditions for persistence, allowing the system and its mode of organization to be prepared for a new situation [20]

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Summary

Introduction

Policies and strategies should aim to optimise both the u-rbanisation process and urban functions and infrastructure in order to achieve sustainable development, maximise urbanisation’s benefits, and reduce urbanisation’s negative impacts [1]. There are studies analysing resilience in the context of urban mobility, they focus on the scarcity of fossil fuels [29,34], and some economic changes are yet to be analysed Based on such studies, we identified a gap regarding the analysis of the levels of resilience and vulnerability in face of subsidy threats that can severely affect developing countries. Here we propose a method to measure the level of resilience and vulnerability based on fuzzy logic and apply it to a real situation through a case study of the subsidy, of fare integration, for public transportation in the city of Rio de Janeiro, Brazil, from the standpoint of job access.

Resilience and Vulnerability Concepts
Urban Resilience
Fuzzy System Modelling
Method Characterization
Study Area and Zoning
Gathering Data
Logical Architecture of the Problem Using Fuzzy Logic
Levels of Vulnerability and Resilience
Vulnerability and Resilience Criteria
Vulnerability and Resilience Codes
Vulnerability and Resilience Maps
Analysis
Conclusions
Implications
Findings
Limitations and Future Work
Full Text
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