论文标题

从过境起源目的地数据推断出护理旅行行为的流动性

Inferring Mobility of Care Travel Behavior From Transit Origin-Destination Data

论文作者

Abdelhalim, Awad, Shuman, Daniela, Stewart, Anson F, Campbell, Kayleigh B, Patel, Mira, de Madariaga, Ines Sanchez, Zhao, Jinhua

论文摘要

性别在公共交通上的旅行行为存在实质性差异。研究得出的结论是,这些差异在很大程度上归因于家庭责任通常不成比例地减少妇女,这导致妇女更有可能将过境用于“护理流动性”的伞概念所指的目的。与过去使用调查和定性数据量化性别影响的过去的研究相反,我们提出了一种新型的数据驱动的工作流,利用先前开发的来源,目的地和转移推理(ODX)的组合,基于个人交易票价交易,基于名称的性别推断以及基于名称的性别推断以及地理空间分析作为确定护理行程的框架。我们将此框架应用于华盛顿大都会地区运输管理局(WMATA)的数据。分析了2019年第一季度进行的数百万次旅行的数据,这项研究的结果表明,我们提出的工作流程可以识别出护理旅行行为的流动性,检测时间和感兴趣的地方,其中女性旅行者在同样采样的子集中(根据推断的性别使用者)的份额比男性高10% - 15%。这项研究中介绍的工作流提供了一个蓝图,用于结合过境原产地点数据,推断客户人口统计以及地理空间分析,使公共交通机构能够在票价卡级别评估不同政策和运营决策的性别影响。

There are substantial differences in travel behavior by gender on public transit. Studies have concluded that these differences are largely attributable to household responsibilities typically falling disproportionately on women, leading to women being more likely to utilize transit for purposes referred to by the umbrella concept of "mobility of care". In contrast to past studies that have quantified the impact of gender using survey and qualitative data, we propose a novel data-driven workflow utilizing a combination of previously developed origin, destination, and transfer inference (ODX) based on individual transit fare card transactions, name-based gender inference, and geospatial analysis as a framework to identify mobility of care trip making. We apply this framework to data from the Washington Metropolitan Area Transit Authority (WMATA). Analyzing data from millions of journeys conducted in the first quarter of 2019, the results of this study show that our proposed workflow can identify mobility of care travel behavior, detecting times and places of interest where the share of women travelers in an equally-sampled subset (on basis of inferred gender) of transit users is 10% - 15% higher than that of men. The workflow presented in this study provides a blueprint for combining transit origin-destination data, inferred customer demographics, and geospatial analyses enabling public transit agencies to assess, at the fare card level, the gendered impacts of different policy and operational decisions.

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