  The ubiquitous growth of mobility-on-demand services for passenger and goods
delivery has brought various challenges and opportunities within the realm of
transportation systems. As a result, intelligent transportation systems are
being developed to maximize operational profitability, user convenience, and
environmental sustainability. The growth of last mile deliveries alongside
ridesharing calls for an efficient and cohesive system that transports both
passengers and goods. Existing methods address this using static routing
methods considering neither the demands of requests nor the transfer of goods
between vehicles during route planning. In this paper, we present a dynamic and
demand aware fleet management framework for combined goods and passenger
transportation that is capable of (1) Involving both passengers and drivers in
the decision-making process by allowing drivers to negotiate to a mutually
suitable price, and passengers to accept/reject, (2) Matching of goods to
vehicles, and the multi-hop transfer of goods, (3) Dynamically generating
optimal routes for each vehicle considering demand along their paths, based on
the insertion cost which then determines the matching, (4) Dispatching idle
vehicles to areas of anticipated high passenger and goods demand using Deep
Reinforcement Learning (RL), (5) Allowing for distributed inference at each
vehicle while collectively optimizing fleet objectives. Our proposed model is
deployable independently within each vehicle as this minimizes computational
costs associated with the growth of distributed systems and democratizes
decision-making to each individual. Simulations on a variety of vehicle types,
goods, and passenger utility functions show the effectiveness of our approach
as compared to other methods that do not consider combined load transportation
or dynamic multi-hop route planning.
