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Item type:Publication, Optimal humanitarian warehouses location considering vulnerability previous condition(Associacao Brasileira de Engenharia de Producao, 2022-03-07)Goal: This study tackles on location problem, analyzing the urban shape base on vulnerability previous conditions in affected areas. Design / Methodology / Approach: A numerical analysis is performed using an integer programming model to find optimal preposition warehouses location. Results: The minimum cost is reached when the needs of all the people affected by the natural disaster are met. Excess construction of warehouses implies additional maintenance and administration expenses, while lack of construction of warehouses implies expenses originated by unsatisfied demand. Limitations of the investigation: Among the main limitations found in this study were the lack of updated data and few studies related to natural disaster prevention in the case study. Practical implications: Organizations should implement the recommendations given by the new model. This is because the model minimizes the cost, since the damage caused by a natural disaster is much more expensive than preventing it. Originality / Value: The novelty in this study is the creation of a punishment factor based on the level of vulnerability of the routes used in transportation. This is to simulate the impact the deterioration of routes has on the response time of sending aid supplies. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Integrated Data-Driven Predictive Resilience Framework for Disaster Evacuation Traffic Management(MDPI, 2023-06-01)Maintaining smooth traffic during disaster evacuation is a lifesaving step. Traffic resilience is often used to define the ability of a roadway during disaster evacuation to withstand and recover its functionality from disturbances in terms of traffic flow caused by a disaster. However, a high level of variances due to system complexity and inherent uncertainty associated with disaster and evacuation risks poses great challenges in predicting traffic resilience during evacuation. To fill this gap, this study aimed to propose a new integrated data-driven predictive resilience framework that enables incorporating traffic uncertainty factors in determining road traffic conditions and predicting traffic performance using machine learning approaches and various space and time (spatiotemporal) data sources. This study employed an augmented Long Short-Term Memory (LSTM)-based approach with correlated spatiotemporal traffic data to predict traffic conditions, then to map those conditions to traffic resilience levels: daily traffic, segment traffic, and overall route traffic. A case study of Hurricane Irma’s evacuation traffic was used to demonstrate the effectiveness of the proposed framework. The results indicated that the proposed method could effectively predict traffic conditions and thus help to determine traffic resilience. The data also confirmed that the traffic infrastructures along the US I-75 route remained resilient despite the disturbances during the disaster evacuation activities. The findings of this study suggest that the proposed framework is applicable to other disaster management scenarios to obtain more robust decisions for the emergency response during disaster evacuation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Global Framings of Pandemic Recovery: Insights Across Conservation, Development and Health Fields(SAGE Publishing, 2026-06-01)In many contexts recovery from COVID-19 is ongoing. The impacts of the pandemic were diverse and their distribution uneven, which may in part explain the diversity in the ways in which its recovery has been framed. Numerous framings concerning what constitutes 'recovery', what its pursuit should entail, who (or what) it should target and whose vision the notion of recovery should represent have been expressed by various fields of study. An assessment of the way in which diverse fields (e.g. health, conservation and development) have represented the priorities of recovery from the COVID-19 pandemic is so far not available. This knowledge gap is important since understanding the threads in common, and those distinct between fields, may help move towards a more integrated appraisal of the multiple priorities salient to recovery. Integration however also involves representing diverse knowledges, values and lived experiences and supporting disaster-resilient communities requires being attentive to the voices of the most marginalized. Due recognition of and engagement with these groups is essential for enhancing the justice and equity of recovery-focused interventions, and can help ensure that interventions do not presume, misplace or misrepresent local priorities. With growing recognition of the need for decolonial, grounded and co-developed responses to processes of recovery, nature futures and global development there is a need to understand how COVID-19 recovery has been conceived and articulated across fields, and crucially, the extent to which it has included the perceptions of socially, economically and politically marginalized groups. We analyzed 30 papers (10 per field), and asked (1) How does COVID-19 recovery tend to be framed within these fields, including the representation of intersecting risks? (2) Where is there divergence and congruence in recovery discourses across these fields, and what would an integrated understanding of recovery look like? (3) To what extent are local voices reflected or acknowledged in these international framings? We found that while perspectives differed, all highlighted how COVID-19 exposed pre-existing interconnected crises. Many framed the root cause as flawed economic growth models, which was considered in need of various degrees of transformation combined with more integrated governance. Crucially, few framings had strong representation of local, or marginalized voices and relatively few papers actively grounded their calls, or prominently advocated for such practices. Our findings point to a need for more co-created knowledge generation and agenda setting for COVID-19 recovery, and disaster recovery more broadly.