Abstract Natural or man-made hazards that require evacuation put already vulnerable populations in a more precarious situation. However, when plans and decisions about evacuation are made, the assumption of access to a private car is typically made and differences in income levels across a community is rarely accounted for. The result is that carless members of a community can find themselves stranded. Low income carless residents need alternative transportation means to reach shelters in case of an emergency. Thus, evacuation plans, decisions and models need necessary information that identifies and locates these populations. In this paper, data from the American Community Survey, US Census, Internal Revenue Services and the National Household Travel Survey are used to generate synthetic population for Anne Arundel County, Maryland using the copula concept. Geographic locations of low-income residents are identified within each subarea of the county (census tract) and their car ownership is estimated with a binomial logit model. The developed population synthesis method will allow officials to have a more accurate account of disadvantaged populations for emergency planning and identify locations of shelters, triage points as well as planning carless transportation services.

中文翻译：

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小区域合成人口生成的统计方法作为无车疏散规划的基础

摘要 需要疏散的自然或人为灾害使本已脆弱的人群处于更加危险的境地。然而，在制定疏散计划和决定时，通常会假设可以使用私家车，而很少考虑到整个社区的收入水平差异。结果是社区中无车的成员可能会发现自己陷入困境。低收入无车居民需要其他交通工具才能在紧急情况下到达避难所。因此，疏散计划、决策和模型需要识别和定位这些人群的必要信息。在本文中，来自美国社区调查、美国人口普查、国税局和全国家庭旅行调查的数据用于生成安妮阿伦德尔县的合成人口，马里兰州使用 copula 概念。在县（人口普查区）的每个分区内确定低收入居民的地理位置，并使用二项对数模型估计他们的汽车拥有量。开发的人口综合方法将使官员能够更准确地了解弱势群体，以进行应急规划，并确定避难所、分流点的位置以及规划无车运输服务。