ABSTRACT We consider an original dynamic dial‐a‐ride service designed for sparsely populated areas. The service relies on vehicles capable of switching between road and an existing abandoned rail network. It defines a Dial‐A‐Ride Problem (DARP) with rail scheduling constraints. In the DARP, a set of users must be picked up and dropped off at desired locations, while adhering to time windows and maximum travel time constraints. In the dynamic context, the system has to evaluate the acceptance of new customers and their integration into vehicle routes. Rail scheduling highly complicates the problem by creating interdependencies between vehicles. We develop a solution method combining the Adaptive Large Neighborhood Search framework, a set‐covering approach, and multiple anticipatory scenarios generated with fictitious requests. We evaluate the effectiveness of our approach with realistic instances generated from a specific abandoned railway in France.