The goal of this study is to assess and quantify the potential employment accessibility benefits of shared-use automated vehicle (AV) mobility service (SAMS) modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study proposes employing a welfare-based (i.e. logsum-based) measure of accessibility, obtained via estimating a hierarchical work destination-commute mode choice model. The employment accessibility logsum measure incorporates the spatial distribution of worker residences and employment opportunities, the attributes of the available commute modes, and the characteristics of individual workers. The study further captures heterogeneity of workers using a latent class analysis (LCA) approach to account for different worker clusters valuing different types of employment opportunities differently, in which the socio-demographic characteristics of workers are the LCA model inputs. The accessibility analysis results in Southern California indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high- and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode; and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits.

这项研究的目的是评估和量化考虑到工作人口异质性的大都市区共享使用自动驾驶汽车（AV）出行服务（SAMS）模式的潜在就业可及性。为了实现这一目标，本研究提出采用基于福利（即基于logum）的可访问性度量，该度量是通过估计分层工作目的地-通勤模式选择模型而获得的。就业可及性对数度量结合了工人居住地和就业机会的空间分布，可用通勤方式的属性以及单个工人的特征。该研究还使用潜在类别分析（LCA）方法来捕获工人的异质性，以解释不同的工人群体，对不同类型的就业机会进行不同的评估，其中工人的社会人口统计学特征是LCA模型的输入。南加利福尼亚州的可及性分析结果表明：（i）各个潜在类别的可及性收益差异不大，但年轻工人和低收入工人确实比高收入和中等收入工人享有更高的福利；（ii）低密度地区的工人比高密度地区的工人受益更多，在获得交通福利方面存在很大的空间差异；（iii）与SAMS + Transit模式相对，几乎所有可访问性优势都来自仅SAMS模式；