Cokyasar, Tanerde Souza, FelipeAuld, JoshuaVerbas, Omer2025-03-172025-03-1720220361-19812169-4052https://doi.org/10.1177/03611981211049422https://hdl.handle.net/20.500.13099/1737Efficient dynamic ride-matching (DRM) in large-scale transportation systems is a key driver in transport simulations to yield answers to challenging problems. Although the DRM problem is simple to solve, it quickly becomes a computationally challenging problem in large-scale transportation system simulations. Therefore, this study thoroughly examines the DRM problem dynamics and proposes an optimization-based solution framework to solve the problem efficiently. To benefit from parallel computing and reduce computational times, the problem's network is divided into clusters utilizing a commonly used unsupervised machine learning algorithm along with a linear programming model. Then, these sub-problems are solved using another linear program to finalize the ride-matching. At the clustering level, the framework allows users adjusting cluster sizes to balance the trade-off between the computational time savings and the solution quality deviation. A case study in the Chicago Metropolitan Area, U.S., illustrates that the framework can reduce the average computational time by 58% at the cost of increasing the average pick up time by 26% compared with a system optimum, that is, non-clustered, approach. Another case study in a relatively small city, Bloomington, Illinois, U.S., shows that the framework provides quite similar results to the system-optimum approach in approximately 62% less computational time.eninfo:eu-repo/semantics/closedAccesspublic transportationinnovative public transportation services and technologiesmobility as a service (MaaS)ruralintercity busspecialized transportationArticle10.1177/0361198121104942226763172182Q3WOS:0007121520000012-s2.0-85128174900Q2