Providing diverse modes of travel facilitates people's access to jobs, healthcare, critical activities, and other services. To assess the equity of access to transportation services, it is essential to consider different travel modes. In this study, we propose a concept called “multi-modal deserts” and develop an approach to identify them. Multi-modal deserts refer to areas with limited mobility options, which restrict people's access to essential services and opportunities. Based on the concept of multi-modality, our methodology integrates Mahalanobis distance for multivariate outlier detection to identify if an area's mobility services significantly deviate from other areas considering road network factors and travel modes. Downtown Tampa, Florida, was selected as an empirical case to demonstrate the proposed method, and 11 multi-modal deserts were identified among 182 Census Block Groups. In addition, spider charts were used to illustrate and compare the characteristics of these multi-modal deserts. The results identified several multi-modal deserts with different poverty levels and transportation constraints. The insights can assist local authorities in identifying mobility gaps, allocating resources more effectively, and improving equal access to opportunities for all residents.

中文翻译：

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识别多模态沙漠：一种多变量异常值检测方法


提供多样化的出行方式有助于人们获得工作、医疗保健、关键活动和其他服务。要评估获得交通服务的公平性，必须考虑不同的出行方式。在这项研究中，我们提出了一个称为 “多峰沙漠 ”的概念，并开发了一种识别它们的方法。多式联运沙漠是指行动不便的地区，这限制了人们获得基本服务和机会的机会。基于多模态的概念，我们的方法集成了马氏距离进行多变量异常值检测，以确定考虑到道路网络因素和出行模式，一个地区的出行服务是否与其他区域显着偏离。佛罗里达州坦帕市中心被选为实证案例来证明所提出的方法，并在 182 个人口普查区块组中确定了 11 个多模式沙漠。此外，还使用了蜘蛛图来说明和比较这些多模态沙漠的特征。结果确定了几个具有不同贫困水平和交通限制的多模式沙漠。这些见解可以帮助地方当局识别流动性差距，更有效地分配资源，并改善所有居民获得机会的平等机会。