Abstract:
Motivated by the growth of ride-sharing services and the technological evolution in
autonomous vehicles (AV), this thesis seeks to develop and assess an operational
platform for an on-demand autonomous vehicle hybrid sharing system (AVHS). The
AVHS system is comprised of a fleet of AVs controlled by a Central Operation Manager
(COM) that provides three levels of service to travelers ranging from a taxi-like
service, to a flexible-route, flexible-schedule transit-like system. The proposed AVHS
system operational platform is built as a dynamic, sequential, and time-dependent
stochastic control problem whose objective is to simultaneously minimize the costs
associated with the operator and the traveler. The system is developed and tested using
an agent-based modeling and a simulation framework where the different agents
and layers of the system interact with each other and react to the surrounding environment.
The multi-layered system consists of three main components: i) the traffic
network layer, ii) the vehicles fleet and the COM layer, and iii) the travelers layer.
This study develops a complex dynamic optimization-based control algorithm for a
dynamic on-demand shared autonomous vehicles operation and tests the system’s
flexibility and resilience, through a sensitivity analysis on different testing cases defined
by the AV fleet size, travel demand rate, and demand composition by levels of
service. The operational performance is assessed against different KPIs from both,
operation and system user perspectives. The system showed its ability to entertain
multiple levels of service where, to the best of the authors’ knowledge, this has not
been achieved before. Results showed that the developed system was able to maintain
the quality of service among different levels of services by reducing the travelers’
waiting time, increasing vehicle occupancy, and reducing the empty vehicles miles
traveled.
