Regional sustainability and transportation sustainability have been intensely discussed and analyzed in recent decades. Though the use of indicators has been adopted in those models, debates continue on what indicators should be used and how to optimize the number of indicators. This results in the lack of a comprehensive and efficient method to assess and compare the sustainability of a sub-system, such as transportation system, and overall regional sustainability. A thorough literature review is conducted to identify indicators used to assess regional sustainability and transportation sustainability. Then, based on the available data, two sets of indicators for regional sustainability and transportation sustainability are identified and calculated respectively for the 382 metropolitan statistical areas (MSAs) in the U.S. A self-organizing map, which is a type of artificial neural network, is used to cluster the MSAs and compare their regional sustainability and transportation sustainability as well as to investigate the relationships among indicators. The results show that MSAs with a higher score on regional sustainability do not necessarily have a higher score on transportation sustainability. Some MSAs that are geographically close to each other have similar scores in regional sustainability and transportation sustainability. These findings provide insights to decision makers that the assessment of sustainability should consider both correlation and heterogeneity of different indicators within a region. Therefore, it is important to develop a comprehensive and efficient method to evaluate the role of sustainability in one urban sub-system, such as transportation, in the overall regional sustainability.

近几十年来，对区域可持续性和交通运输可持续性进行了广泛的讨论和分析。尽管在这些模型中采用了指标的使用，但是关于应使用哪些指标以及如何优化指标数量的争论仍在继续。这导致缺乏一种综合有效的方法来评估和比较子系统的可持续性，例如交通运输系统的可持续性，以及整个区域的可持续性。进行了全面的文献综述，以识别用于评估区域可持续性和交通运输可持续性的指标。然后，根据可用数据，分别为美国的382个大都市统计区（MSA）识别并计算出两组区域可持续性和交通可持续性的指标。这是一种人工神经网络，用于对MSA进行聚类，比较其区域可持续性和交通可持续性，并研究指标之间的关系。结果表明，在地区可持续性方面得分较高的MSA不一定在交通可持续性方面得分较高。在地理上彼此接近的某些MSA在区域可持续性和交通可持续性方面得分相似。这些发现为决策者提供了见识，即对可持续性的评估应考虑区域内不同指标的相关性和异质性。因此，开发一种全面而有效的方法来评估可持续性在一个城市子系统（例如交通，