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

arXiv: https://arxiv.org/abs/2009.09367 · HTML: https://mlsanity.com/paper/2009.09367/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [Transfer learning for cross-modal demand prediction of bike-share and public transit](https://mlsanity.com/paper/2203.09279/) (2022)
- [Predicting travel demand of a bike sharing system using graph convolutional neural networks](https://mlsanity.com/paper/2408.09317/) (2024)
- [T-STAR: A Context-Aware Transformer Framework for Short-Term Probabilistic Demand Forecasting in Dock-Based Shared Micro-Mobility](https://mlsanity.com/paper/2602.06866/) (2026)

## Abstract (arXiv)

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.