  Taxi services and product delivery services are instrumental for our modern
society. Thanks to the emergence of sharing economy, ride-sharing services such
as Uber, Didi, Lyft and Google's Waze Rider are becoming more ubiquitous and
grow into an integral part of our everyday lives. However, the efficiency of
these services are severely limited by the sub-optimal and imbalanced matching
between the supply and demand. We need a generalized framework and
corresponding efficient algorithms to address the efficient matching, and hence
optimize the performance of these markets. Existing studies for taxi and
delivery services are only applicable in scenarios of the one-sided market. In
contrast, this work investigates a highly generalized model for the taxi and
delivery services in the market economy (abbreviated as"taxi and delivery
market") that can be widely used in two-sided markets. Further, we present
efficient online and offline algorithms for different applications. We verify
our algorithm with theoretical analysis and trace-driven simulations under
realistic settings.
