Modeling bike counts in a bike-sharing system considering the effect of weather conditions

TL;DR

This paper presents a novel approach using Random Forest and regression techniques to quantify how weather conditions influence bike counts in a large bike-sharing system, highlighting the significance of temperature, humidity, and temporal factors.

Contribution

It introduces a method that effectively models bike counts considering weather effects, emphasizing the importance of geographic-specific weather variables and temporal factors.

Findings

Time-of-day, temperature, and humidity are key predictors.

Weather variables are location-dependent and should be quantified accordingly.

Bike availability at previous time points strongly influences current counts.

Abstract

The paper develops a method that quantifies the effect of weather conditions on the prediction of bike station counts in the San Francisco Bay Area Bike Share System. The Random Forest technique was used to rank the predictors that were then used to develop a regression model using a guided forward step-wise regression approach. The Bayesian Information Criterion was used in the development and comparison of the various prediction models. We demonstrated that the proposed approach is promising to quantify the effect of various features on a large BSS and on each station in cases of large networks with big data. The results show that the time-of-the-day, temperature, and humidity level (which has not been studied before) are significant count predictors. It also shows that as weather variables are geographic location dependent and thus should be quantified before using them in modeling. Further, findings show that the number of available bikes at station i at time t-1 and time-of-the-day were the most significant variables in estimating the bike counts at station i.