  This paper focuses on modeling ride requests and their variations over
location and time, based on analyzing extensive real-world data from a
ride-sharing service. We introduce a graph model that captures the spatial and
temporal variability of ride requests and the potentials for ride pooling. We
discover these ride request graphs exhibit a well known property called
densification power law often found in real graphs modelling human behaviors.
We show the pattern of ride requests and the potential of ride pooling for a
city can be characterized by the densification factor of the ride request
graphs. Previous works have shown that it is possible to automatically generate
synthetic versions of these graphs that exhibit a given densification factor.
We present an algorithm for automatic generation of synthetic ride request
graphs that match quite well the densification factor of ride request graphs
from actual ride request data.
