Evacuation Shelter Scheduling Problem

• URL: http://arxiv.org/abs/2111.13326v1
• Date: Fri, 26 Nov 2021 06:15:32 GMT
• Title: Evacuation Shelter Scheduling Problem
• Authors: Hitoshi Shimizu, Hirohiko Suwa, Tomoharu Iwata, Akinori Fujino, Hiroshi Sawada, Keiichi Yasumoto
• Abstract summary: Evacuation shelters are urgently required during natural disasters. The larger the scale of the disaster, the more costly it becomes to operate shelters. We propose a method that estimates movement costs based on the numbers of evacuees and shelters during an actual disaster.
• Score: 37.310358982178506
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Evacuation shelters, which are urgently required during natural disasters, are designed to minimize the burden of evacuation on human survivors. However, the larger the scale of the disaster, the more costly it becomes to operate shelters. When the number of evacuees decreases, the operation costs can be reduced by moving the remaining evacuees to other shelters and closing shelters as quickly as possible. On the other hand, relocation between shelters imposes a huge emotional burden on evacuees. In this study, we formulate the "Evacuation Shelter Scheduling Problem," which allocates evacuees to shelters in such a way to minimize the movement costs of the evacuees and the operation costs of the shelters. Since it is difficult to solve this quadratic programming problem directly, we show its transformation into a 0-1 integer programming problem. In addition, such a formulation struggles to calculate the burden of relocating them from historical data because no payments are actually made. To solve this issue, we propose a method that estimates movement costs based on the numbers of evacuees and shelters during an actual disaster. Simulation experiments with records from the Kobe earthquake (Great Hanshin-Awaji Earthquake) showed that our proposed method reduced operation costs by 33.7 million dollars: 32%.

Related papers

• GREAT-EER: Graph Edge Attention Network for Emergency Evacuation Responses [3.854471865029609]
  Emergency situations that require the evacuation of urban areas can arise from man-made causes (e.g., terrorist attacks or industrial accidents) or natural disasters.<n>In this work, we identify and propose the Bus Evacuation Orienteering Problem (BEOP), an NP-hard optimization problem.<n>The purpose of bus-based evacuation is to reduce congestion and disorder that arises in purely car-focused evacuation scenarios.
  arXiv  Detail & Related papers  (2026-02-16T12:04:14Z)
• An Optimized Evacuation Plan for an Active-Shooter Situation Constrained by Network Capacity [0.0]
  Emergency evacuation is intensely stressful, which along with the lack of verifiable real-time information may lead to fatal incorrect decisions.<n>We develop a multi-route routing optimization algorithm that determines multiple optimal safe routes for each evacuee.<n>Overall, our algorithm reduces the total casualties by 34.16% and 53.3%, compared to our previous routing algorithm without capacity constraints and an expert-advised routing strategy respectively.
  arXiv  Detail & Related papers  (2025-04-29T18:06:00Z)
• Evacuation patterns and socioeconomic stratification in the context of wildfires in Chile [1.5157842912803314]
  We use high-definition mobile phone records to analyse evacuation patterns during the wildfires in Valpara'iso, Chile. We find that many people spent nights away from home, with those in the lowest socioeconomic segment stayed away the longest. Our results show that socioeconomic differences play a role in evacuation dynamics, providing useful insights for response planning.
  arXiv  Detail & Related papers  (2024-10-08T13:18:49Z)
• CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics [50.122541222825156]
  This study introduces a novel approach to disaster text classification by enhancing a pre-trained Large Language Model (LLM)<n>Our methodology involves creating a comprehensive instruction dataset from disaster-related tweets, which is then used to fine-tune an open-source LLM.<n>This fine-tuned model can classify multiple aspects of disaster-related information simultaneously, such as the type of event, informativeness, and involvement of human aid.
  arXiv  Detail & Related papers  (2024-06-16T23:01:10Z)
• Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning [11.697034536189094]
  We present an innovative algorithm that constructs Neyman stratified random sampling trees for binary classification. Our findings demonstrate that our method surpasses both passive and conventional active learning techniques. It effectively addresses the'sampling bias' challenge in traditional active learning strategies.
  arXiv  Detail & Related papers  (2024-05-28T01:34:35Z)
• CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification [51.58605842457186]
  We present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data.
  arXiv  Detail & Related papers  (2023-10-23T07:01:09Z)
• DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank [52.20298962359658]
  In crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. fully-supervised approaches require annotating vast amounts of data and are impractical due to limited response time. Semi-supervised models can be biased, performing moderately well for certain classes while performing extremely poorly for others. We propose a simple but effective debiasing method, DeCrisisMB, that utilizes a Memory Bank to store and perform equal sampling for generated pseudo-labels from each class at each training.
  arXiv  Detail & Related papers  (2023-10-23T05:25:51Z)
• Estimation of Human Condition at Disaster Site Using Aerial Drone Images [10.271448515653276]
  We investigate a method to automatically estimate the damage status of people based on their actions in aerial drone images. We constructed a new dataset of aerial images of human actions in a hypothetical disaster that occurred in an urban area. The results showed that the status with characteristic human actions could be classified with a recall rate of more than 80%.
  arXiv  Detail & Related papers  (2023-08-08T18:57:01Z)
• Robust Bayesian Subspace Identification for Small Data Sets [91.3755431537592]
  We propose regularized estimators, shrinkage estimators and Bayesian estimation to reduce the effect of variance. Our experimental results show that our proposed estimators may reduce the estimation risk up to $40%$ of that of traditional subspace methods.
  arXiv  Detail & Related papers  (2022-12-29T00:29:04Z)
• A Machine learning approach for rapid disaster response based on multi-modal data. The case of housing & shelter needs [0.0]
  One of the most immediate needs of people affected by a disaster is finding shelter. This paper proposes a machine learning workflow that aims to fuse and rapidly analyse multimodal data. Based on a database of 19 characteristics for more than 200 disasters worldwide, a fusion approach at the decision level was used.
  arXiv  Detail & Related papers  (2021-07-29T18:22:34Z)
• Constructing Evacuation Evolution Patterns and Decisions Using Mobile Device Location Data: A Case Study of Hurricane Irma [5.902556437760098]
  This paper utilized a large mobile phone Location-Based Services (LBS) data to construct the evacuation pattern during the landfall of Hurricane Irma. We studied users' evacuation decisions, departure and reentry date distribution, and destination choice. Our analysis revealed the importance of the individuals' mobility behavior in modeling the evacuation decision choice.
  arXiv  Detail & Related papers  (2021-02-24T23:24:10Z)
• Evacuation decisions in response to natural disasters: Insights from a large-scale social media survey [1.4999444543328289]
  We leverage a novel method of data collection through social media to explore the evacuation decisions of people in areas affected by the Australian bushfires.<n>We identify key factors influencing household decisions on evacuation, specifically focusing on the phenomenon of household members evacuating or returning from evacuation at different times.
  arXiv  Detail & Related papers  (2020-08-09T05:55:26Z)
• Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
  We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
  arXiv  Detail & Related papers  (2020-06-23T03:36:19Z)
• Congestion-aware Evacuation Routing using Augmented Reality Devices [96.68280427555808]
  We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations. A population density map, obtained on-the-fly by aggregating locations of evacuees from user-end Augmented Reality (AR) devices, is used to model the congestion distribution inside a building.
  arXiv  Detail & Related papers  (2020-04-25T22:54:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.