Investigating Optimal Carpool Scheme by a Semi-Centralized Ride-Matching Approach

Carpooling service obtains a significant interest in both industry and academic fields for its potential to improve mobility and save travel costs. However, the existing approaches for exploring online carpooling routes for riders struggle with the trade-off between system optimality and computation efficiency. This study is motivated to develop a semi-centralized ride-matching strategy (SCM) to address this difficulty. Specifically, we model the carpooling service problem (CSP) by a mixed-integer programming (CSP-MIP), which explores an optimal carpooling solution for a large-scale of riders to minimize system service time by using a small CAV fleet to serve all the riders. To address the computation difficulty, we first analyze and quantify the carpooling chances (C2) among riders according to their trip features. According to the C2, we decompose a large-scale CSP-MIP involving all riders in a network into small sub-MIPs, each including a small number of riders in a carpooling community, without over sacrificing the system optimality. For each sub-MIP, we develop a network flow algorithm combined with a greedy ride-matching model to explore a local optimal solution. The experiments built upon the Hardee network demonstrated that the SCM could solve the CSP efficiently. It significantly improves the computation efficiency while maintaining the system performance as it’s compared to the existing approaches.