City-level circuity factors have been introduced to quantify and compare the directness of vehicular travel across different cities. While these city-level factors help to improve the quality of distance approximation functions for city-wide vehicle movements, more granular factors are needed to obtain accurate shortest path distance approximations for last-mile transportation systems that are typically characterized by local trips. More importantly, local circuity factors encode valuable information about the efficiency and complexity of the urban road network, which can be leveraged to inform policy and practice. In this paper, we quantify and analyze local network circuity leveraging contemporary traffic datasets. Using the city of São Paulo as our primary case study and a combination of supervised and un-supervised machine learning methods, we observe significant heterogeneities in local network circuity, explained by dimensional and topological properties of the road network. Locally, real trip distances are about twice as long as distances predicted by the $L_1$ norm. Results from São Paulo are compared to seven additional urban areas in Latin America and the United States. At a coarse-grained level of analysis, we observe similar correlations between road network properties and local circuity across these cities.

引入了城市级巡回因素来量化和比较不同城市之间的车辆行驶方向。虽然这些城市级别的因素有助于提高城市范围内车辆行驶的距离近似函数的质量，但仍需要更多的粒度因子来获得通常以本地出行为特征的最后一英里运输系统的最短路径距离近似值。更重要的是，当地的巡回因素对有关城市道路网的效率和复杂性的有价值的信息进行编码，可以利用这些信息来指导政策和实践。在本文中，我们利用当代交通数据集来量化和分析局域网的电路。将圣保罗市作为我们的主要案例研究，并结合了有监督和无监督的机器学习方法，我们通过路网的尺寸和拓扑特性观察到了本地网络电路中的大量异质性。就本地而言，实际旅行距离约为乘员预测的距离的两倍。${\mathrm{大号}}_{\mathrm{1个}}$规范。将圣保罗的结果与拉丁美洲和美国的另外七个城市区域进行了比较。在粗粒度的分析水平上，我们观察到这些城市的路网属性与局部环路之间存在相似的相关性。