Intermodal networks offer much flexibility in transport planning, and have the potential to efficiently consolidate goods, even if these goods have distinct pickup locations and destinations. Typically, there is an abundant amount of feasible routes and consolidation opportunities, which makes it challenging to quickly identify good solutions. We propose a planning algorithm for dynamic pickup- and delivery problems in intermodal networks, where freight is consolidated by means of reloads to reduce both costs and emissions. Based on an enumerative arc-expansion procedure, a large number of intermodal routes is generated for each order, of which we store the k best. We subsequently evaluate consolidation opportunities for the k best routes by applying a decision tree structure, taking into account reload operations, timetables, and synchronization of departure windows. Compared to direct road transport, numerical experiments on various virtual problem instances show an average cost saving of 34 %, and an average reduction in CO 2 emissions of 30 %. Furthermore, we test our algorithm on a real-life case of a leading logistics service provider based in the Netherlands, which yields significant benefits as well, both in terms of costs and environmental impact.