While dynamic ridesharing has been extensively studied, there remains a significant research gap in exploring role flexibility within the many-to-many ridesharing scheme, where the system allows for several pickups for drivers and multiple transfers for riders. Previous works have predominantly assumed that all participants own a car and have focused on one-to-one arrangements. Additionally, there is a scarcity of research on integrating High Occupancy Vehicle (HOV) lanes and mathematical modelling. This study addresses these gaps by presenting a novel Mixed Integer Linear Programming (MILP) model that allows for role flexibility irrespective of car ownership and considers the implications of HOV lanes. Computational analysis highlights the benefits of incorporating role flexibility and accommodating non-car-owning participants in many-to-many ridesharing systems. Yet, excessive role shifts may create imbalances, impacting service to non-car owners. Further research should explore these correlations.