Wang, M. , Chen, Z., Rong, H. H., Mu, L., Zhu, P. and Shi, Z. (2022) Ridesharing accessibility from the human eye: Spatial modeling of built environment with street-level images. Computers, Environment and Urban Systems, 97, 101858. (doi: 10.1016/j.compenvurbsys.2022.101858)
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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.
Item Type: | Articles |
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Additional Information: | This study was partially supported by the University of Glasgow Reinvigorating Research Funding (Grant No. 201644-20) and Research Enabling/Impact Generating Scheme (Grant No. 126438-01); Innovation and Technology Commission, the Government of the Hong Kong Special Administrative Region (Grant No. GSP/035/20); the Public Policy Research (PPR) Grant Scheme, PICO, Hong Kong SAR Government (Grant No. 2021.A7.021.21C); the Natural Science Foundation of Guangdong Province (Grant No. 2021A1515011250); and the 2022 Guangzhou Basic Research Scheme. |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Wang, Dr Mingshu |
Authors: | Wang, M., Chen, Z., Rong, H. H., Mu, L., Zhu, P., and Shi, Z. |
College/School: | College of Science and Engineering > School of Geographical and Earth Sciences |
Journal Name: | Computers, Environment and Urban Systems |
Publisher: | Elsevier |
ISSN: | 0198-9715 |
ISSN (Online): | 1873-7587 |
Published Online: | 05 August 2022 |
Copyright Holders: | Copyright © 2022 The Authors |
First Published: | First published in Computers, Environment and Urban Systems 97: 101858 |
Publisher Policy: | Reproduced under a Creative Commons licence |
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