This article presents a modeling framework for the operations of on‐demand mobility services (ODMS) in urban areas. The framework provides the capabilities to analyze ODMS operations while representing emerging services such as ridesharing and transfer. The problem is formulated as a mixed integer program and an efficient decomposition‐based methodology is developed for its solution. The methodology adopts a modified version of the column generation algorithm, which implements iterative decomposition and network augmentation techniques to allow studying networks of moderate size. The results of a set of experiments considering grid and real‐world networks are presented. The results show that increasing the number of passengers willing to rideshare and/or transfer improves the overall performance of ODMS as it increases number of served passengers and associated profit and reduces the number of used vehicles. Although presented as an offline planning tool, the methodology could be adopted for real‐time applications as adequate computational resources become available.

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