Ruijie “Rebecca” Bian, Pamela Murray-Tuite, Joseph E Trainor, Praveen Edara, Konstantinos Triantis
{"title":"依次建模家庭住宿、目的地和出发时间选择","authors":"Ruijie “Rebecca” Bian, Pamela Murray-Tuite, Joseph E Trainor, Praveen Edara, Konstantinos Triantis","doi":"10.1177/02807270231211834","DOIUrl":null,"url":null,"abstract":"During evacuations, households make a number of important, related choices including accommodation type, destination, and departure time. They may make trade-offs among these choices where one decision affects the others. The analysis models the linkages among these three aforementioned choices using data from a household behavioral intention survey conducted in 2017 in the Hampton Roads, VA area. Statistical tests and a theoretical basis show that the approach that best fits the dataset was to estimate the three choices in a sequence, where the first decision serves as an independent variable in the next choice process. To model the sequence, we began by modeling accommodation choice using a multinomial logit (MNL) model. Next, the accommodation choice decisions were used with other control variables to estimate destination choice in a second MNL model. Last, evacuation distance (related to destination decisions) was used in a Cox proportional-hazards model to estimate departure time choices. The models that provide the best estimates included the following control variables that help explain the sequence of decisions residents in the Hampton Roads area expect to make: (1) a variable expressing residential stability helps explain accommodation choice; (2) prior evacuation experience, the geographic location of a household, and the duration of living in the area help predict the destination choice; and (3) distance to the chosen destination helps predict departure time. Findings from this study provide evidence that the decisions associated with these three choices influence each other and help emergency managers identify additional actions that potentially can improve the evacuation experiences of local residents.","PeriodicalId":84928,"journal":{"name":"International journal of mass emergencies and disasters","volume":" 18","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sequentially modeling household accommodation, destination, and departure time choices\",\"authors\":\"Ruijie “Rebecca” Bian, Pamela Murray-Tuite, Joseph E Trainor, Praveen Edara, Konstantinos Triantis\",\"doi\":\"10.1177/02807270231211834\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"During evacuations, households make a number of important, related choices including accommodation type, destination, and departure time. They may make trade-offs among these choices where one decision affects the others. The analysis models the linkages among these three aforementioned choices using data from a household behavioral intention survey conducted in 2017 in the Hampton Roads, VA area. Statistical tests and a theoretical basis show that the approach that best fits the dataset was to estimate the three choices in a sequence, where the first decision serves as an independent variable in the next choice process. To model the sequence, we began by modeling accommodation choice using a multinomial logit (MNL) model. Next, the accommodation choice decisions were used with other control variables to estimate destination choice in a second MNL model. Last, evacuation distance (related to destination decisions) was used in a Cox proportional-hazards model to estimate departure time choices. The models that provide the best estimates included the following control variables that help explain the sequence of decisions residents in the Hampton Roads area expect to make: (1) a variable expressing residential stability helps explain accommodation choice; (2) prior evacuation experience, the geographic location of a household, and the duration of living in the area help predict the destination choice; and (3) distance to the chosen destination helps predict departure time. Findings from this study provide evidence that the decisions associated with these three choices influence each other and help emergency managers identify additional actions that potentially can improve the evacuation experiences of local residents.\",\"PeriodicalId\":84928,\"journal\":{\"name\":\"International journal of mass emergencies and disasters\",\"volume\":\" 18\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of mass emergencies and disasters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/02807270231211834\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of mass emergencies and disasters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/02807270231211834","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sequentially modeling household accommodation, destination, and departure time choices
During evacuations, households make a number of important, related choices including accommodation type, destination, and departure time. They may make trade-offs among these choices where one decision affects the others. The analysis models the linkages among these three aforementioned choices using data from a household behavioral intention survey conducted in 2017 in the Hampton Roads, VA area. Statistical tests and a theoretical basis show that the approach that best fits the dataset was to estimate the three choices in a sequence, where the first decision serves as an independent variable in the next choice process. To model the sequence, we began by modeling accommodation choice using a multinomial logit (MNL) model. Next, the accommodation choice decisions were used with other control variables to estimate destination choice in a second MNL model. Last, evacuation distance (related to destination decisions) was used in a Cox proportional-hazards model to estimate departure time choices. The models that provide the best estimates included the following control variables that help explain the sequence of decisions residents in the Hampton Roads area expect to make: (1) a variable expressing residential stability helps explain accommodation choice; (2) prior evacuation experience, the geographic location of a household, and the duration of living in the area help predict the destination choice; and (3) distance to the chosen destination helps predict departure time. Findings from this study provide evidence that the decisions associated with these three choices influence each other and help emergency managers identify additional actions that potentially can improve the evacuation experiences of local residents.