  The potential of an efficient ride-sharing scheme to significantly reduce
traffic congestion, lower emission level, as well as facilitating the
introduction of smart cities has been widely demonstrated. This positive thrust
however is faced with several delaying factors, one of which is the volatility
and unpredictability of the potential benefit (or utilization) of ride-sharing
at different times, and in different places. In this work the following
research questions are posed: (a) Is ride-sharing utilization stable over time
or does it undergo significant changes? (b) If ride-sharing utilization is
dynamic, can it be correlated with some traceable features of the traffic? and
(c) If ride-sharing utilization is dynamic, can it be predicted ahead of time?
We analyze a dataset of over 14 Million taxi trips taken in New York City. We
propose a dynamic travel network approach for modeling and forecasting the
potential ride-sharing utilization over time, showing it to be highly volatile.
In order to model the utilization's dynamics we propose a network-centric
approach, projecting the aggregated traffic taken from continuous time periods
into a feature space comprised of topological features of the network implied
by this traffic. This feature space is then used to model the dynamics of
ride-sharing utilization over time. The results of our analysis demonstrate the
significant volatility of ride-sharing utilization over time, indicating that
any policy, design or plan that would disregard this aspect and chose a static
paradigm would undoubtably be either highly inefficient or provide insufficient
resources. We show that using our suggested approach it is possible to model
the potential utilization of ride sharing based on the topological properties
of the rides network. We also show that using this method the potential
utilization can be forecasting a few hours ahead of time.
