  With the rapid development of smart mobile devices, the car-hailing platforms
(e.g., Uber or Lyft) have attracted much attention from both the academia and
the industry. In this paper, we consider an important dynamic car-hailing
problem, namely \textit{maximum revenue vehicle dispatching} (MRVD), in which
rider requests dynamically arrive and drivers need to serve as many riders as
possible such that the entire revenue of the platform is maximized. We prove
that the MRVD problem is NP-hard and intractable. In addition, the dynamic
car-hailing platforms have no information of the future riders, which makes the
problem even harder. To handle the MRVD problem, we propose a queueing-based
vehicle dispatching framework, which first uses existing machine learning
algorithms to predict the future vehicle demand of each region, then estimates
the idle time periods of drivers through a queueing model for each region. With
the information of the predicted vehicle demands and estimated idle time
periods of drivers, we propose two batch-based vehicle dispatching algorithms
to efficiently assign suitable drivers to riders such that the expected overall
revenue of the platform is maximized during each batch processing. Through
extensive experiments, we demonstrate the efficiency and effectiveness of our
proposed approaches over both real and synthetic datasets.
