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Public transit systems are crucial to mobility and access in cities throughout the world. This article addresses the importance of these transit systems in San Antonio, Texas. We show how transit systems exacerbate race and class inequality and the accessibility of city spaces with a focus on San Antonio’s buses. Using mixed methods (surveys, interviews, ethnography, and document analysis) we illustrate that poor and working-class Latinx communities experience reduced access to resource rich areas of the city when they are dependent upon the city’s public transportation. To better describe this experience we use the concepts, enclaves of exclusion and enclaves of inaccessibility. Our findings show that mobility through San Antonio for poor and working class Latinxs is limited especially for people in these communities who rely on public transit. This experience with these public transit systems often renders them as individuals who do not belong in certain neighborhoods, and ultimately reinforces the longstanding histories of race and class segregation in San Antonio.
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K-Prototypes Segmentation Analysis on Large-Scale Ridesourcing Trip Data
Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they affect travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examined emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data were matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcing’s role in Chicago’s mobility system. The goal was to investigate the systematic variations in patronage of ridehailing. K-prototypes was utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output was a classification of the data into several clusters called prototypes. Six ridesourcing prototypes were identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses the implications of the identified clusters related to affordability, equity, and competition with transit.
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- Award ID(s):
- 1847537
- PAR ID:
- 10136258
- Publisher / Repository:
- Sage
- Date Published:
- Journal Name:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume:
- 2674
- Issue:
- 9
- ISSN:
- 0361-1981
- Page Range / eLocation ID:
- 383 to 394
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/0361198120929338
