  Transportation network companies (TNCs) have become a highly utilized
transportation mode over the past years. At their emergence, TNCs were serving
ride requests one by one. However, the economic and environmental benefits of
ridesharing encourages them to dynamically pool multiple ride requests to
enable people to share vehicles. In a dynamic ridesharing (DRS) system, a fleet
operator seeks to minimize the overall travel cost while a rider desires to
experience a faster (and cheaper) service. While the DRS may provide relatively
cheaper trips by pooling requests, the service speed is contingent on the
objective of the vehicle-to-rider assignments. Moreover, the operator must
quickly assign a vehicle to requests to prevent customer loss. In this study we
develop an iterative assignment (IA) algorithm with a balanced objective to
conduct assignments quickly. A greedy algorithm from the literature is also
tailored to further reduce the computational time. The IA was used to measure
the impact on service quality of fleet size; assignment frequency; the weight
control parameter of the two objectives on vehicle occupancy -- rider wait time
and vehicle hours traveled. A case study in Austin, TX, reveals that the key
performance metrics are the most sensitive to the weight parameter in the
objective function.
