  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.
