Ridesharing: The Impact of Traffic Restrictions and Role Flexibility.

Ridesharing has been widely acknowledged as an effective solution for reducing traffic congestion and pollution. However, in cities with high levels of air pollution, there are often restrictions on vehicle movements that have been overlooked in ridesharing studies. This study aims to investigate the effects of such restrictions on optimal matching and route changes. We first propose a Mixed Integer Linear Programming (MILP) model to solve the many-to-many ridesharing problem under traffic restrictions. Then, we introduce a variant of the model with role flexibility for drivers. The results emphasize that traffic restrictions have a minimal impact on the overall number of matches. However, in contrast to previous studies that solely examined role flexibility among car owners, which resulted in more matches, our findings show a decline in overall matches when not all participants possess a vehicle. This is due to the potential savings associated with role shifting, leading to an increased level of demand and a decrease in supply.