Ridesharing accessibility from the human eye: Spatial modeling of built environment with street-level images
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 co...
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| Published in | Computers, environment and urban systems Vol. 97; p. 101858 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.10.2022
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| Subjects | |
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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.
•Street-level imageries are used to measure the built environments at a finer resolution.•Ridesharing accessibility has a spatial relationship with pick-up locations' built environment.•Larger salient regions are associated with shorter waiting times for UberX, UberXL and UberSELECT.•Road network density is more related to ridesharing accessibility than the proportion of roads. |
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| AbstractList | 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.
•Street-level imageries are used to measure the built environments at a finer resolution.•Ridesharing accessibility has a spatial relationship with pick-up locations' built environment.•Larger salient regions are associated with shorter waiting times for UberX, UberXL and UberSELECT.•Road network density is more related to ridesharing accessibility than the proportion of roads. |
| ArticleNumber | 101858 |
| Author | Rong, Helena Hang Zhu, Pengyu Wang, Mingshu Chen, Zheyan Shi, Zenglin Mu, Lan |
| Author_xml | – sequence: 1 givenname: Mingshu surname: Wang fullname: Wang, Mingshu email: Mingshu.Wang@glasgow.ac.uk organization: School of Geographical & Earth Sciences, University of Glasgow, United Kingdom – sequence: 2 givenname: Zheyan surname: Chen fullname: Chen, Zheyan email: z.chen1@uu.nl organization: Department of Human Geography and Spatial Planning, Utrecht University, The Netherlands – sequence: 3 givenname: Helena Hang surname: Rong fullname: Rong, Helena Hang email: hhr2112@columbia.edu organization: Graduate School of Architecture, Planning and Preservation, Columbia University, United States – sequence: 4 givenname: Lan surname: Mu fullname: Mu, Lan email: mulan@uga.edu organization: Department of Geography, University of Georgia, United States – sequence: 5 givenname: Pengyu surname: Zhu fullname: Zhu, Pengyu email: pengyuzhu@ust.hk organization: Division of Public Policy, Hong Kong University of Science and Technology, Hong Kong SAR, China – sequence: 6 givenname: Zenglin surname: Shi fullname: Shi, Zenglin email: z.shi@uva.nl organization: Video & Image Sense Lab, University of Amsterdam, The Netherlands |
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| Keywords | Uber Built environment Accessibility Street-level image Ridesharing |
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| Title | Ridesharing accessibility from the human eye: Spatial modeling of built environment with street-level images |
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