Optimizing Carpool Scheduling Algorithm through Partition Merging

The rapidly increasing number of vehicles in roads leads to numerous problems in metropolitan areas. Several researchers show that carpooling can be an efficient solution to relieve the pressures caused by large numbers of cars. Previous research on carpools introduces several additional constraints to simplify the problem, but some of them are unreasonable in reality. In this paper, we focus on removing the static capacity constraint. Doing so allows a vehicle to carry more passengers than vehicle’s capacity, which is possible if some people are dropped off and new passengers take their places during the journey. A greedy approach based on multi-round matching is proposed, and it is further improved by taking advantage of geometry properties. We apply our algorithms to both simulated and real world datasets, and experiment results show that our algorithms have better performances than existing approaches.