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📝 Related papers/other publications
[1] Lee, Y., Cooverji, F., & Yuan, Y. (2025). Exploring the spatiotemporal patterns of shared bicycle usage: A case study of MetroBike in Austin, Texas. Computational Urban Science, 5(52). [Link]
MetoroBike usage pattern and accessibility
MetoroBike usage pattern and accessibility
Analysis flow map
Fig 1. Analysis flow map
Usage pattern analysis using community detection method
Fig 2. Communities with similar usage patterns
Fig 3. MetroBike usage pattern by hour across communities
Using OD mobility data from MetroBike, the public bike-share system in Austin, TX, we analyzed communities with similar usage patterns. When considering all trip records, four distinct communities emerged, which were classified as residential areas, downtown, UT Austin, and leisure districts (Fig 2). The hourly usage patterns for each community are shown in Fig 3.
MetroBike accessibility and disparities
Fig 4. Accessibility score to MetroBike kiosks calculated using the 2SFCA method
Fig 5. Lorenz curve of accessibility score
Accessibility, calculated using the 2SFCA method, is relatively high around the UT Austin campus and downtown, while most other areas show low levels of accessibility (Fig. 4). The Lorenz curve, which visualizes inequality, indicates that spatial accessibility to MetroBike is highly unequal across the city (Fig 5).
📐 Two steps of 2SFCA method:
Step 1:
Step 2:
Where Sj represents the service capacity at location j; Pk denotes the population at demand location k; dij is the distance or travel time between demand location iii and service location j; d0 is the catchment threshold that defines the maximum acceptable distance or travel time; Rj is the supply-to-demand ratio calculated for each service location in Step 1; and Ai is the accessibility score at demand location i, obtained in Step 2 by summing all Rj values from service locations within the catchment area of i.
GWR analysis of socioeconomic variables influencing accessibility scores
Fig 6. Coefficient of proportion of commuters by bicycle, walking, and public transportation
Fig 7. Coefficient of employment ratio
According to the GWR results, the proportion of commuters using bicycles, walking, and public transportation (positive effect, Fig 6) and the employment ratio (negative effect, Fig 7) were identified as the factors that have relatively strong impacts on accessibility.
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