The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Not Minding the Gap: Does Ride-Hailing Serve Transit Deserts?
Open accessPreprint — published as 10.1016/j.jtrangeo.2020.102918
Abstract
Transit has long connected people to opportunities but access to transit varies greatly across space. In some cases, unevenly distributed transit supply creates gaps in service that impede travelers' abilities to cross space and access jobs or other opportunities. With the advent of ride-hailing services like Uber and Lyft, however, travelers now have a new potential to gain automobility without high car purchase costs and in the absence of reliable transit service. Research remains mixed on whether ride-hailing serves as a modal complement or substitute to transit or whether ride-hailing fills transit service needs gaps. This study measures transit supply in Chicago and compares it to ride-hailing origins and destinations to examine if ride-hailing fills existing transit service gaps. Findings reveal clustering of ride-hailing pickups and drop-offs across the City of Chicago, but that the number of ride-hailing pickups and drop-offs was most strongly associated with high neighborhood median household income rather than measures of transit supply. In bivariate analyses, transit service was not associated with ride-hailing trip ends. But after controlling for neighborhood socioeconomic status, transit dependency, population density, and employment density, we found fewer ride-hailing trips in neighborhoods where bus service dominated and significantly more ride-hailing trips where rail service was prevalent. Patterns were slightly different for overnight weekend ride-hailing p…
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In other words, TNCs appear to not be serving transit deserts. Instead, they are filling gaps in public transit schedules.”
From Ready to ride: security and transit-related determinants of ride-hailing adoption in Latin America (2022) · cites this paper as Barajas and Brown, 2020
Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,987
Local Indicators of Spatial Association—LISA
Anselin, L. (2010) · Geographical Analysis · doi:10.1111/j.1538-4632.1995.tb00338.x · Cited by 12,925
An Introduction to Spatial Econometrics
LeSage, J. P. (2008) · Revue d économie industrielle · doi:10.4000/rei.3887 · Cited by 3,047
GeoDa : An Introduction to Spatial Data Analysis
Anselin, L., Syabri, I., & Kho, Y. (2005) · Geographical Analysis · doi:10.1111/j.0016-7363.2005.00671.x · Cited by 2,666
The Color of Law: A Forgotten History of How Our Government Segregated America
Pattillo, M. E. (2018) · Contemporary Sociology A Journal of Reviews · doi:10.1177/0094306118792220ll · Cited by 2,263
Applied Spatial Data Analysis with R - Second Edition
Bivand, R., Pebesma, E., & Gómez-Rubio, V. (2008) · Use R! · doi:10.1007/978-1-4614-7618-4 · Cited by 2,065
Applied Spatial Econometrics: Raising the Bar
Elhorst, J. P. (2010) · Spatial Economic Analysis · doi:10.1080/17421770903541772 · Cited by 1,798
Rayle, L., Dai, D., Chan, N., Cervero, R., & Shaheen, S. (2015) · Transport Policy · doi:10.1016/j.tranpol.2015.10.004 · Cited by 1,220
Introduction: Spatial Econometrics
Griffith, D. A., & Paelinck, J. H. P. (2010) · Advances in geographic information science · doi:10.1007/978-3-642-16043-1_10 · Cited by 923
Why do the poor live in cities? The role of public transportation
Glaeser, E. L., Kahn, M. E., & Rappaport, J. M. (2007) · Journal of Urban Economics · doi:10.1016/j.jue.2006.12.004 · Cited by 916
Cited by
Li, Z. (2022) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2022.101845 · Cited by 1,307
Mobility as a service (MaaS): A thematic map of challenges and opportunities
Alyavina, E., Nikitas, A., & Njoya, E. T. (2022) · Research in Transportation Business & Management · doi:10.1016/j.rtbm.2022.100783 · Cited by 98
Alyavina, E., Nikitas, A., & Njoya, E. T. (2022) · Transport Policy · doi:10.1016/j.tranpol.2024.03.001 · Cited by 54
Ride-sourcing compared to its public-transit alternative using big trip data
Liao, Y. J. (2021) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2021.103135 · Cited by 53
Sharing behavior in ride-hailing trips: A machine learning inference approach
Taiebat, M., Amini, E., & Xu, M. (2022) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2021.103166 · Cited by 52
Metadata
- Authors
- Barajas, J., & Brown, A.
- Affiliations
- University of California, Davis (US)
- University of Oregon (US)
- Corresponding author
- Barajas, Jesus
- Venue
- Journal of Transport Geography
- Publisher
- Center for Open Science
- Published
- 2020-05-30
- Volume
- 90
- Pages
- 102918-102918
- ISSN
- 0966-6923 (ISSN-L)
- OpenAlex
- W3106404186
- Cited by
- 81
- References
- 59
- Citations by year
- 2026: 14 · 2025: 15 · 2024: 16 · 2023: 15 · 2022: 11 · 2021: 10
- FWCI
- 3.33
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Transport Geography (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- SocArXiv (OSF Preprints) (submittedVersion, cc-by-nc-nd)
- SocArXiv (OSF Preprints) (submittedVersion)
- Funders
- University of California, Davis
- Topics
- Social Sciences › Transportation › Transportation and Mobility Innovations
- Social Sciences › Transportation › Transportation Planning and Optimization
- Social Sciences › Transportation › Urban Transport and Accessibility
- Keywords
- ride-hailing, transit deserts, modal substitution, transit supply, bus services, railway services, neighborhood socioeconomic status, low-income communities, Chicago, Uber, Lyft
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
