  With the emergence of e-hailing taxi services, a growing number of scholars
have attempted to analyze the taxi trips data to gain insights from drivers'
and passengers' flow patterns and understand different dynamics of urban public
transportation. Existing studies are limited to passengers' location analysis
e.g., pick-up and drop-off points, in the context of maximizing the profits or
better managing the resources for service providers. Moreover, taxi drivers'
locations at the time of pick-up requests and their pickup performance in the
spatial-temporal domain have not been explored. In this paper, we analyze
drivers' and passengers' locations at the time of booking request in the
context of drivers' pick-up performances. To facilitate our analysis, we
implement a modified and extended version of a co-clustering technique, called
sco-iVAT, to obtain useful clusters and co-clusters from big relational data,
derived from booking records of Grab ride-hailing service in Singapore. We also
explored the possibility of predicting timely pickup for a given booking
request, without using entire trajectories data. Finally, we devised two
scoring mechanisms to compute pickup performance score for all driver
candidates for a booking request. These scores could be integrated into a
booking assignment model to prioritize top-performing drivers for passenger
pickups.
