Recently, ridesharing applications, such as Uber, have gained popularity by helping riders to save money. However, current real-time ridesharing systems with time-window constraints have not yet considered the impact of real-time road conditions. In this work, we propose transferring algorithms and incentive models to enhance the flexibility of an existing large scale real-time ridesharing system. Transferring algorithms aim to transfer those users who experienced delay to other vehicles by using a pairwise bounding-box pruning technique. The incentive models avoid the use of transferring but instead compensate affected passengers with credits to maintain their satisfaction level in a different way. Extensive experiments were conducted using a real road network. Results show that our proposed algorithms and models are able to boost users' satisfaction level up to 90% under the influence of real-time road conditions.