Quantifying the impact of urban road networks on the efficiency of local trips

Daniel Merchán,Matthias Winkenbach,André Snoeck

doi:10.1016/j.tra.2020.02.015

Abstract

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 Sao 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 Sao 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.

Highlights

Analytical approximation methods are widely used to quantify travel distances of vehicles within a transportation system
Real trip distances are about twice as long as distances predicted by the L1 norm
Local circuity factors capture these complex interactions in a simple measure, which can be used to improve shortest path distance approximations, and to better understand how the topological and physical properties of Circuity factor c One-way fraction (%) Highway length Primary road length Node degree Node connectivity Betweenness centrality Fraction of urban area Amb. population

Summary

Introduction

Analytical approximation methods are widely used to quantify travel distances of vehicles within a transportation system. Analytical distance approximations are useful to inform decisions related to the strategic design and planning of transportation and logistics systems In such decisions, the focus of the analysis lies less on an exact result for a specific realization of customers to be served, but more on the expected performance of the system. In the design of urban transportation systems, the so-called L1 or rectilinear norm is a common distance metric assumed when analytically approximating vehicular travel distances within the underlying road network. This norm assumes that the road network resembles a perfectly rectangular lattice.

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