  The emergence of on-demand ride pooling services allows each vehicle to serve
multiple passengers at a time, thus increasing drivers' income and enabling
passengers to travel at lower prices than taxi/car on-demand services (only one
passenger can be assigned to a car at a time like UberX and Lyft). Although
on-demand ride pooling services can bring so many benefits, ride pooling
services need a well-defined matching strategy to maximize the benefits for all
parties (passengers, drivers, aggregation companies and environment), in which
the regional dispatching of vehicles has a significant impact on the matching
and revenue. Existing algorithms often only consider revenue maximization,
which makes it difficult for requests with unusual distribution to get a ride.
How to increase revenue while ensuring a reasonable assignment of requests
brings a challenge to ride pooling service companies (aggregation companies).
In this paper, we propose a framework for vehicle dispatching for ride pooling
tasks, which splits the city into discrete dispatching regions and uses the
reinforcement learning (RL) algorithm to dispatch vehicles in these regions. We
also consider the mutual information (MI) between vehicle and order
distribution as the intrinsic reward of the RL algorithm to improve the
correlation between their distributions, thus ensuring the possibility of
getting a ride for unusually distributed requests. In experimental results on a
real-world taxi dataset, we demonstrate that our framework can significantly
increase revenue up to an average of 3\% over the existing best on-demand ride
pooling method.
