Dynamic Control and Modelling of Ride-Sourcing Systems in Large Urban Cities

The goal of the thesis is to develop a holistic framework to improve the service quality of a ride-sourcing system in terms of reducing waiting and unassigned time of the ride-sourcing vehicles and reducing waiting and unassigned time the passengers. To this end, the research approach deals with three main intertwine problems: designing a vehicle-passenger matching method, modelling of a ride-sourcing system in a macroscopic level, and designing a proactive controller for repositioning of idle vehicles. The specific objectives of this thesis are (i) designing an adaptive spatio-temporal matching method to dynamically find optimum values for vehicle-passenger maximum matching distance and the frequency of the matchings, (ii) developing a validated macroscopic model with capabilities of considering vehicle-passenger matching method and repositioning of idle vehicles to predict the evolution of the state of ride-sourcing system, and (iii) designing a Nonlinear Model Predictive Controller (NMPC) for repositioning of the idle vehicles proactively to the locations with higher probability of being matched to the waiting passengers. The microsimulation results demonstrate accuracy of the model in predicting the evolution of the number of the ride-sourcing vehicles in different states (e.g. idle, transferred, dispatched, and occupied) and passengers (e.g. waiting and assigned) in each region of the network. Furthermore, the proposed matching method pinpoints its effectiveness by reducing reserved and delay times of ride-sourcing vehicles and passengers. In microsimulation experiments, the designed controller improves the performance of the ride-sourcing system by reducing passengers’ average unassigned time (-20.4%) and waiting times (-12.4%), vehicles’ average waiting times (-8.8%), the number of the fleet size (-18.6%) and increasing the number of the served trip requests (9.7%)