Network and station-level bike-sharing system prediction: a San Francisco bay area case study

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2009.09367.pdf (756.77 KB)

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https://www.tandfonline.com/doi/abs/10.1080/15472450.2021.1948412

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https://doi.org/10.1080/15472450.2021.1948412
 http://hdl.handle.net/11603/26205

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UMBC Data Science
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Author/Creator ORCID

https://orcid.org/0000-0002-6835-8338

Date

2021-07-08

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journal articles
preprints
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Citation of Original Publication

Huthaifa I. Ashqar, Mohammed Elhenawy, Hesham A. Rakha, Mohammed Almannaa & Leanna House (2022) Network and station-level bike-sharing system prediction: a San Francisco bay area case study, Journal of Intelligent Transportation Systems, 26:5, 602-612, DOI: 10.1080/15472450.2021.1948412

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This is the submitted manuscript of an article published by Taylor & Francis in Journal of Intelligent Transportation Systems on 08 Jul 2021, available online: http://www.tandfonline.com/https://doi.org/10.1080/15472450.2021.1948412

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Abstract

The paper develops models for modeling the availability of bikes in the San Francisco Bay Area Bike Share System applying machine learning at two levels: network and station. Investigating BSSs at the station-level is the full problem that would provide policymakers, planners, and operators with the needed level of details to make important choices and conclusions. We used Random Forest and Least-Squares Boosting as univariate regression algorithms to model the number of available bikes at the station-level. For the multivariate regression, we applied Partial Least-Squares Regression (PLSR) to reduce the needed prediction models and reproduce the spatiotemporal interactions in different stations in the system at the network-level. Although prediction errors were slightly lower in the case of univariate models, we found that the multivariate model results were promising for the network-level prediction, especially in systems where there is a relatively large number of stations that are spatially correlated. Moreover, results of the station-level analysis suggested that demographic information and other environmental variables were significant factors to model bikes in BSSs. We also demonstrated that the available bikes modeled at the station-level at time 𝑡 had a notable influence on the bike count models. Station neighbors and prediction horizon times were found to be significant predictors, with 15 minutes being the most effective prediction horizon time.