- The paper proposes an integrated formulation combining ride-matching and vehicle rebalancing to optimize shared mobility operations.
- It employs a grid network representation using open-source data to simultaneously account for supply and demand dynamics.
- Results show significant improvements in passenger wait times and fleet utilization over traditional sequential approaches.
The paper "An Integrated Ride-Matching Model for Shared Mobility on Demand Services" addresses the growing interest and operational potential of shared mobility on demand (MoD) services, such as UberPool and LyftLine. These services, which allow multiple passengers to share rides, are becoming increasingly popular as they offer both economic and environmental advantages. Moreover, the introduction of autonomous vehicles (AVs), which enable a fully connected and centrally managed fleet, further enhances these opportunities.
The authors identify two critical tasks for the effectiveness of shared MoD services: ride-matching, which pairs passengers with vehicles, and vehicle rebalancing, which ensures that vehicles are optimally distributed across the service area to meet demand. Traditionally, these tasks have been handled separately and sequentially, which can lead to suboptimal performance and lower service quality.
In this paper, the authors propose a novel integrated approach to combine ride-matching and vehicle rebalancing into a single cohesive formulation. By integrating these two tasks, the model leverages the interactions between them, potentially leading to more efficient and effective service operations.
The proposed methodology uses a grid representation of the city network, which allows for the incorporation of supply-level information. This integrated model is designed to optimize both the matching of rides and the repositioning of vehicles in a way that considers the dynamic nature of supply and demand in real-time.
To evaluate the effectiveness of their approach, the authors conduct a comparative case paper using an open-source data set. They benchmark their integrated model against a conventional sequential approach. The results of the paper demonstrate that the integrated model not only serves at least the same number of passengers but also achieves significant improvements in various service quality and sustainability metrics. These improvements include better passenger wait times and fleet utilization, which collectively enhance the overall level of service.
In summary, this paper presents a significant advancement in the management of shared mobility services by proposing an integrated model that efficiently combines ride-matching and vehicle rebalancing tasks. The findings suggest that such an integrated approach can lead to higher service levels and sustainability benefits, making it a promising strategy for the future of shared mobility systems.