Mobility-on-Demand (MoD) systems offer a flexible mobility alternative to classical public transportation services in urban areas. However, a significant part of MoD vehicles operating time can be spent waiting empty or driving to reach new potential ride requests. Improving vehicle fleet operation is an extremely challenging problem, as the number of vehicles in operation at a time cannot be controlled. To cope with this issue, new forms of mobility are being deployed successfully: for instance, ride-sharing enabled MoD systems can match riders from several requests. Existing work considers that the best way to achieve significant performance is to control vehicles. However, travel times are hard to predict in congested traffic, and optimizing a relocation scheme of empty vehicles can be hard for large-scale networks and big fleets. In this paper, we take the perspective of riders that collaborate with other travellers in order to walk to locations where they are more likely to get picked up by a MoD system. We introduce a multi-agent model that accounts for vehicles, riders and the MoD platform. The aim of this interactionbased model is to enable riders to dynamically form emergent microcommunities that physically meet, wait and share a vehicle together for part of their trip. Our approach is evaluated in a simulation framework that allows to investigate the respective behaviour of vehicles and riders. Ride requests are generated from New York City taxi dataset. We show that our approach allows riders to improve their chance to be picked up and reduce their travel costs while improving overall efficiency of the fleet.