  The growth in online goods delivery is causing a dramatic surge in urban
vehicle traffic from last-mile deliveries. On the other hand, ride-sharing has
been on the rise with the success of ride-sharing platforms and increased
research on using autonomous vehicle technologies for routing and matching. The
future of urban mobility for passengers and goods relies on leveraging new
methods that minimize operational costs and environmental footprints of
transportation systems.
  This paper considers combining passenger transportation with goods delivery
to improve vehicle-based transportation. Even though the problem has been
studied with a defined dynamics model of the transportation system environment,
this paper considers a model-free approach that has been demonstrated to be
adaptable to new or erratic environment dynamics. We propose FlexPool, a
distributed model-free deep reinforcement learning algorithm that jointly
serves passengers & goods workloads by learning optimal dispatch policies from
its interaction with the environment. The proposed algorithm pools passengers
for a ride-sharing service and delivers goods using a multi-hop transit method.
These flexibilities decrease the fleet's operational cost and environmental
footprint while maintaining service levels for passengers and goods. Through
simulations on a realistic multi-agent urban mobility platform, we demonstrate
that FlexPool outperforms other model-free settings in serving the demands from
passengers & goods. FlexPool achieves 30% higher fleet utilization and 35%
higher fuel efficiency in comparison to (i) model-free approaches where
vehicles transport a combination of passengers & goods without the use of
multi-hop transit, and (ii) model-free approaches where vehicles exclusively
transport either passengers or goods.
