What question did this study set out to answer?

To assess how demographic factors, built environment characteristics, and station network density influence station-level bikesharing ridership across multiple urban systems.

January 1, 2013Transportation Research Record Journal of the Transportation Research Board

Station-Level Forecasting of Bikesharing Ridership

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To assess how demographic factors, built environment characteristics, and station network density influence station-level bikesharing ridership across multiple urban systems.
Analyzed station-level ridership data across three operational municipal bikesharing systems in the United States.
Developed multicity regression models integrating demographic variables, built environment attributes, weather measures, and spatial network features.
Station ridership correlated positively with population density, retail job density, transit/bike/walk commuter shares, median income, education levels, and bikeway presence.
Station ridership exhibited statistically significant negative associations with days of precipitation and the proportion of nonwhite residents.
Proximity to a broader network of neighboring bikesharing stations consistently showed a strong, statistically significant positive correlation with station-level demand across model specifications.

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Implication

Cross-sectional multicity analysis reveals built environment and station network density predict bikesharing ridership across urban stations, highlighting the importance of connected station networks.

Cite This Study

R. Alexander Rixey (2013) studied this question.

synapsesocial.com/papers/6a1bcfe51567d2fc4d5f0e4a https://doi.org/10.3141/2387-06

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Station-Level Forecasting of Bikesharing Ridership

Key Points

Discussion

Implication

Cite This Study

Station-Level Forecasting of Bikesharing Ridership

Key Points

Discussion

Implication

Cite This Study

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