This post continues the exploratory analysis of bike sharing data from Kaggles competition. From the last post, I ended trying to predict the general upward trend through time of total bike rentals. One idea I posted there was that maybe the number of casual riders could be used to predict changes in later total rentals. For example, if the service is growing in popularity people might start by renting as a casual (non registered) rider, grow to like the service and eventually become registered themselves. The following code chunk generates the monthly plot of total, casual and registered rentals. The code that comes before it is located in the initial Kaggle post.
The first notable trend from this plot is that casual rentals seem to be flat on season. For example, winter months seem to have the same average number of casual rentals independent of year. Therefore, the growth in total rentals is driven by the growth in registered rentals. The ideal data set would include the transmission from casual to registered, but I don’t have that. However, given that casual riders are relatively flat on season, and if casual riders convert to registered riders at a somewhat constant rate, I could expect to see continued trend growth in total rentals at the same rate. If on the other hand casual riders were increasing on season, one might expect to see total rentals increasing at an increasing rate.