Optimization-based Control Algorithm: Development and Testing for Dynamic On-Demand SAV Operation

Motivated by the growth of ride-sharing services and the technological evolution in autonomous vehicles (AV), this study 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 traveller. This study develops a complex dynamic optimization-based control algorithm for a dynamic on-demand shared autonomous vehicle 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. 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 number of empty vehicles miles traveled.