  Consumers are creatures of habit, often periodic, tied to work, shopping and
other schedules. We analyzed one month of data from the world's largest
bike-sharing company to elicit demand behavioral cycles, initially using models
from animal tracking that showed large customers fit an Ornstein-Uhlenbeck
model with demand peaks at periodicities of 7, 12, 24 hour and 7-days. Lorenz
curves of bicycle demand showed that the majority of customer usage was
infrequent, and demand cycles from time-series models would strongly overfit
the data yielding unreliable models. Analysis of thresholded wavelets for the
space-time tensor of bike-sharing contracts was able to compress the data into
a 56-coefficient model with little loss of information, suggesting that
bike-sharing demand behavior is exceptionally strong and regular. Improvements
to predicted demand could be made by adjusting for 'noise' filtered by our
model from air quality and weather information and demand from infrequent
riders.
