Network and Station-Level Bike-Sharing System Prediction: A San Francisco Bay Area Case Study

• URL: http://arxiv.org/abs/2009.09367v1
• Date: Sun, 20 Sep 2020 06:46:41 GMT
• Title: Network and Station-Level Bike-Sharing System Prediction: A San Francisco Bay Area Case Study
• Authors: Huthaifa I. Ashqar, Mohammed Elhenawy, Hesham A. Rakha, Mohammed Almannaa, and Leanna House
• Abstract summary: The paper develops models for modeling the availability of bikes in the San Francisco Bay Area Bike Share System. It applies machine learning two levels: network and station.
• Score: 12.477331187546216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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