  The demand for e-hailing services is growing rapidly, especially in large
cities. Uber is the first and popular e-hailing company in the United Stated
and New York City. A comparison of the demand for yellow-cabs and Uber in NYC
in 2014 and 2015 shows that the demand for Uber has increased. However, this
demand may not be distributed uniformly either spatially or temporally. Using
spatio-temporal time series models can help us to better understand the demand
for e-hailing services and to predict it more accurately. This paper analyzes
the prediction performance of one temporal model (vector autoregressive (VAR))
and two spatio-temporal models (Spatial-temporal autoregressive (STAR); least
absolute shrinkage and selection operator applied on STAR (LASSO-STAR)) and for
different scenarios (based on the number of time and space lags), and applied
to both rush hours and non-rush hours periods. The results show the need of
considering spatial models for taxi demand.
