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

TL;DR

This study develops machine learning models to predict bike availability in the San Francisco Bay Area Bike Share System at both network and station levels, aiding decision-making for system management.

Contribution

It introduces multivariate PLSR models for network-level prediction and highlights the importance of demographic and environmental factors at the station level.

Findings

Multivariate models show promising network-level prediction accuracy.

Demographic and environmental variables significantly influence bike availability.

15-minute prediction horizon yields the best results.

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.