Working Paper
Publication Date
July 2019
Abstract

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 unsupervised 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 L1 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.