Community-based carpooling has the potential to alleviate traffic congestion and reduce the transportation carbon footprint. Once technology, communication, demographic, and economic barriers are overcome, community-based carpooling can be fully exploited. One of the major barriers to implementation is the difficulty of optimizing carpool formation in large systems. This study utilizes two different methods to solve the carpooling optimization problem: 1) bipartite algorithm and 2) integer linear programming. The bipartite method determines the maximum number of carpooling pairs given acceptable reroute costs and travel delays. The linear programming method defines the most optimal performance that minimizes the most vehicular travel mileage. These two methods are carefully compared to evaluate the carpooling potentials among single-occupancy vehicles based on the output of activity-based model’s (ARC ABM) home-to-work single-occupancy vehicle (SOV) trips that can be paired together towards designated regional employment centers. The experiment showed that under strict assumptions, an upper bound of around 13.6% of such trips could carpool together. The results are promising in terms of higher-than-anticipated carpool match rates and the predicted decrease in total vehicle mileage. Moreover, the framework is flexible enough with the potential to act as a simulation testbed, to optimize vehicular operations, and to match potential carpool partners in real-time.
