IMPROVING EVACUATION PLANNING AND SHELTER SITE SELECTION FOR FLOOD DISASTER: THAI FLOODING CASE STUDY

Evacuation planning and shelter site selection are the most important function of disaster management for the purpose of helping at-risk persons to avoid or recover from the effect of a disaster. This study aims to propose a stochastic linear mixed-integer mathematical programming model for improving flood evacuation planning and shelter site selection under a hierarchical evacuation concept. The hierarchical evacuation concept is applied in this study that balances the preparedness and risk despite the uncertainties of flood events. This study considers the distribution of shelter sites and communities, evacuee\u27s behavior, utilization of shelter and capacity restrictions of the shelter by minimizing total population-weighted travel distance. We conduct computational experiments to illustrate how the proposed methodical model works on a real case problem in which we proposed Thai flooding case study. Also, we perform a sensitivity analysis on the parameters of the mentioned mathematical model and discuss our finding. This study will be a great significance in helping policymakers consider the spatial aspect of the strategic placement of flood shelters and evacuation planning under uncertainties of flood scenarios

Facility location optimization model for emergency humanitarian logistics

Since the 1950s, the number of natural and man-made disasters has increased exponentially and the facility location problem has become the preferred approach for dealing with emergency humanitarian logistical problems. To deal with this challenge, an exact algorithm and a heuristic algorithm have been combined as the main approach to solving this problem. Owing to the importance that an exact algorithm holds with regard to enhancing emergency humanitarian logistical facility location problems, this paper aims to conduct a survey on the facility location problems that are related to emergency humanitarian logistics based on both data modeling types and problem types and to examine the pre- and post-disaster situations with respect to facility location, such as the location of distribution centers, warehouses, shelters, debris removal sites and medical centers. The survey will examine the four main problems highlighted in the literature review: deterministic facility location problems, dynamic facility location problems, stochastic facility location problems, and robust facility location problems. For each problem, facility location type, data modeling type, disaster type, decisions, objectives, constraints, and solution methods will be evaluated and real-world applications and case studies will then be presented. Finally, research gaps will be identified and be addressed in further research studies to develop more effective disaster relief operations

A study on shelter airport selection during large-scale volcanic disasters using CARATS open dataset

Air transport supports economic growth and prosperity through the movement of passengers and goods. As it grows more extensive and more complex, the more vulnerable its operations to unexpected natural disasters (e.g., typhoon and volcanic eruption) will become. In Japan, many active volcanos are affecting its airspace and considered a threat to the national air transportation and critical aviation equipment such as aircraft. The study focuses on solving the evacuation of the affected aircraft during the volcanic eruption by proposed the shelter airport selection model using the historical data of volcanic eruption, aircraft movement, and airports data in Japan. The model was applied to the genetic algorithm (GA), the metaheuristic algorithm to provide the approximate solution of the suitable shelter airport with minimum flight time and no exceeded shelter airports\u27 capacities for the aircraft evacuation

AN INTEGRATED MULTI-MODEL OPTIMIZATION AND FUZZY AHP FOR SHELTER SITE SELECTION AND EVACUATION PLANNING

Due to an increasing severity of recent disasters, shelter site selection and evacuation planning have become an essential function for the purpose of helping at-risk persons to avoid or recover from the effect of a disaster. Therefore, this study aims to propose an integrated mathematical optimization and fuzzy analytic hierarchy process for shelter site selection and evacuation planning. The mathematical models are formulated under different constraints and model types, in which the objective of each mathematical model is to minimize the total travel distance. The mathematical models are coded and run in optimizer tool for creating plans. Then, Fuzzy Analytic Hierarchy Process is applied to choose the appropriate plan under uncertainty and vagueness of the expert\u27s opinion. A numerical example with a real case study of a Banta municipality in Thailand is given to demonstrate the application of our conceptual model. This study will be great significance in helping decision makers consider placement of emergency shelters and evacuation planning with respect to both qualitative and quantitative measurement. Moreover, our study can be a guide of the methodology to be implemented to other problems as well

Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

The rise of radiofrequency scanner technology has led to its potential application in the observation of people’s movements. This study used aWi-Fi scanner device to track tourists’ traveling behavior inHokkaido’s tourismarea,whichoccupies a large regionthat features auniquenatural landscape. Inbound tourists have significantly increased in recent years; thus, tourism’s sustainability is considered to be important formaintaining the tourismatmosphere in the long term. Using internet-enabled technology to conduct extensive area surveys can overcome the limitations imposed by conventional methods. This study aims to use digital footprint data to describe and understand traveler mobility in a large tourism area in Hokkaido. Association rule mining (ARM)—a machine learning methodology—was performed on a large dataset of transactions to identify the rules that link destinations visited by tourists. This process resulted in the discovery of traveling patterns that revealed the association rules between destinations, and the attractiveness of the destinations was scored on the basis of visiting frequency, with both inbound and outbound movements considered. A visualization method was used to illustrate the relationships between destinations and simplify the mathematical descriptions of traveler mobility in an attractive tourism area. Hence, mining the attractiveness of destinations in a large tourism area using an ARMmethod integrated with aWi-Fi mobility tracking approach can provide accurate information that forms a basis for developing sustainable destination management and tourism policies

Post-disaster waste management with carbon tax policy consideration

Generally, the activities of post-disaster waste management usually produce high carbon emissions, which can cause damage to the environment. However, the issue of carbon emissions in the post-disaster waste supply chain is neglected. Hence, this paper aims to propose a mixed-integer linear programming model to address the post-disaster waste processing supply chain network design problem with the consideration of a carbon tax policy. The proposed model is developed based on the concept of a mixed strategy of waste separation to reduce carbon emissions. Not only the carbon emission perspective but also the financial perspective for post-disaster waste supply chain management is determined in the objective function. The proposed model was verified and validated by employing a numerical example based on realistic data. Based on the numerical example, the results show that the implementation of a carbon tax policy with the mixed strategy for waste separation can reduce carbon emissions in the post-disaster waste supply chain efficiently. (C) 2021 The Authors. Published by Elsevier Ltd

AN INTEGRATED MULTI-MODEL OPTIMIZATION AND FUZZY AHP FOR SHELTER SITE SELECTION AND EVACUATION PLANNING

Due to an increasing severity of recent disasters, shelter site selection and evacuation planning have become an essential function for the purpose of helping at-risk persons to avoid or recover from the effect of a disaster. Therefore, this study aims to propose an integrated mathematical optimization and fuzzy analytic hierarchy process for shelter site selection and evacuation planning. The mathematical models are formulated under different constraints and model types, in which the objective of each mathematical model is to minimize the total travel distance. The mathematical models are coded and run in optimizer tool for creating plans. Then, Fuzzy Analytic Hierarchy Process is applied to choose the appropriate plan under uncertainty and vagueness of the expert\u27s opinion. A numerical example with a real case study of a Banta municipality in Thailand is given to demonstrate the application of our conceptual model. This study will be great significance in helping decision makers consider placement of emergency shelters and evacuation planning with respect to both qualitative and quantitative measurement. Moreover, our study can be a guide of the methodology to be implemented to other problems as well

Development of downscaling method using the RBF network assessing the hourly population inflow: A case study of the Sapporo urban area

In Japan in recent years, policies for compact cities have been promoted as the population has decreased, and the use of micro-geo data has attracted attention in urban planning. Therefore, when considering a compact city, it is important to know the relationship between the urban facility layout and the population flow. In this research, we created a data set using demographic data, location information of mobile phones, and detailed building data and used a radial basis function (RBF) network. In short, the purpose of this study was to develop a method to reduce the estimated area of population inflow per hour. Population inflow is expressed as the visiting population, which is defined by the difference in the staying population in the time of two sections. By spatially visualizing the results, we were able to downscale the population flow data on a 500 m grid

Multi-objective two-stage stochastic optimization model for post-disaster waste management

Post-disaster waste management is one of the most crucial tasks in the recovery phase of the disaster cycle, and it was created to assist affected communities in returning to a stable state following a disaster. To develop an efficient post-disaster waste management strategy, this study presents a multi-objective two-stage stochastic mixed integer linear programming model for post-disaster waste management. The proposed mathematical model was developed based on a mixed strategy of on-site and off-site waste separation in the supply chain. This study aims to minimize not only the total cost and the environmental impact to provide waste flow decisions and choose collection and separation sites, recycling sites, landfill sites, and incineration sites throughout the supply chain under the uncertain situation. To solve a multi-objective problem, a normalized weighted sum method is used to find the solution. A numerical case based on realistic data is presented to validate and verify the proposed model. Based on the numerical example, the results demonstrated that the implementation of the mixed strategy for waste separation with the consideration of uncertain situations can reduce the total cost, balance the environmental impact, and determine the unexpected situation in the post-disaster waste supply chain efficiently

Data on changes in travel destination preferences of Thai domestic travelers before and after the COVID-19 pandemic

This data article analyzes the changes in travel habits and destination preferences among Thai domestic travelers before and after the COVID-19 pandemic. The data was collected through an online survey conducted on Facebook, Line, and Instagram, with a sample size of 460 valid respondents. The article provides descriptive statistics and frequency data on travel behavior and attitudes related to various tourist attractions before and after the onset of the pandemic. These insights can be valuable for transportation and tourist destination management in Thailand, as they can be used to compare with other studies using similar methods and outcomes and help to develop specialized and targeted solutions for addressing changes in travel trends and demand after the pandemic. For more information, see the full article titled “Using factor analyses to understand the post-pandemic travel behavior in domestic tourism through a questionnaire survey.