Over the past decade, transportation network companies (TNCs) have disrupted the urban transportation landscape. These services are replacing many traditional vehicle for hire services within people's daily lives. Uber is at the leading edge of this development, with a presence in cities on every continent. In this study, we have analyzed aggregated Uber trip data during 2018 for Miami Metropolitan Area (MMA). Specifically, data on trip source and destination were utilized to visualize connectivity at the census tract level. We further examined the influence of socio-demographic and land use patterns on these levels of connectivity. The results of our analysis found clustering of higher connectivity in census tracts adjacent to major travel thoroughfares. These transit adjacent census tracts had a tendency towards a higher spatial influence as a trip destination. In addition, the results of a geographically weighted regression (GWR) indicated a substantial role of percent commercial land use on tract level connectivity throughout the study area. The findings of our study demonstrate the relevance of detailed trip data from TNCs for a more in-depth understanding of human movement and efficient transportation planning at the local level.

在过去十年中，交通网络公司 (TNC) 扰乱了城市交通格局。这些服务正在取代人们日常生活中的许多传统车辆租赁服务。优步处于这一发展的前沿，在各大洲的城市都有业务。在本研究中，我们分析了 2018 年迈阿密都会区 (MMA) 的汇总优步出行数据。具体来说，旅行来源和目的地的数据被用来可视化人口普查区域级别的连通性。我们进一步研究了社会人口和土地利用模式对这些连通性水平的影响。我们的分析结果发现，在与主要交通干道相邻的人口普查区中，较高的连通性聚集在一起。这些过境相邻的人口普查区作为旅行目的地有更高的空间影响力。此外，地理加权回归 (GWR) 的结果表明，商业土地使用百分比对整个研究区域的土地级别连通性具有重要作用。我们的研究结果表明，跨国公司的详细旅行数据对于更深入地了解地方一级的人员流动和有效交通规划具有相关性。