An Efficient Profit-Aware Scalable Vehicle Dispatch Framework for On-Demand Ridesharing

On-demand ridesharing is a promising avenue to transform urban mobility by providing effective, low-cost transportation services to passengers in real-time while increasing transit companies' profits. However, existing works suffer from the lack of balance between the system profit and the response time. Therefore, this paper proposes a computationally efficient, dynamic vehicle dispatch framework while optimizing system profit. We implemented a one-to-one assignment method coupled with a feasible schedule pruning strategy to enhance the proposed method's performance. Additionally, a profit optimization mechanism that dynamically estimates the fare per passenger based on travel time is presented. This fare estimation strategy is more realistic since it incorporates varying traffic conditions. Extensive experiments conducted on the New York City taxicab open-source data demonstrate that the proposed framework is up to ten times faster than the state-of-the-art method and achieves comparable profit. Moreover, the proposed approach proved scalable and efficient for large problem instances.