Demand-responsive transit (DRT) leverages its customizable and flexible advantages to better align with hub operations, emerging as a potential solution for integrated transportation hub connection service challenges. However, considering reserved and real-time demands, the DRT scheduling process becomes complex for boarding and alighting passengers. This study establishes a dynamic and static DRT scheduling method for hub-feeder connections. The model incorporates all scheduling participants, including reserved demands, real-time demands, boarding/alighting demands, and time window constraints. Then, an adaptive large neighborhood search (ALNS) algorithm is designed to solve the problem, and the proposed model is validated by a case study based on the Nanjing South integrated transportation hub, China. The results show that the proposed method can not only meet the alighting passenger need but also effectively improve the service level of boarding passengers, thereby increasing vehicle utilization and operational revenue. The results provide a reference for operators to formulate DRT scheduling schemes in integrated transportation hub connections.
Zhang et al. (2026) studied this question.