Ridesharing accessibility from the human eye: Spatial modeling of built environment with street-level images

Abstract

Scholarly interest in the accessibility of ridesharing services stems from debates within the transportation and planning communities on the inequality of access to transit and the growing digital divide embedded within novel forms of transit services. Contributing to such discussions, this paper considers the city of Atlanta as a case study and explores the links between the spatial disparity of accessibility to different Uber ridesharing products and features of the built environment extracted from Google Street View (GSV) imagery. The variability of wait time for an Uber service is used as a proxy of accessibility, while semantic image segmentation is performed on GSV imagery using a deep learning model DeepLabv3+ to identify notable spatial features captured at the eye-level perspective around service pick-up points. Results from spatial models show that proportions of built environment features such as buildings, vegetation, and terrains are associated with longer waiting times. In contrast, larger salient regions with foreground features are associated with shorter waiting times for several Uber service products.