Freight transport is a significant contributor to global carbon emissions. Shifting freight volumes from road transport towards less carbon-intensive alternatives, such as rail or waterway transport, is an effective strategy to decarbonize freight transport operations. Yet, this modal shift is hindered. Sustainable transport modes are often perceived to be inferior, as they are typically slower, less flexible, and potentially less reliable, than road transport. In order to promote the modal shift, synchromodal transport planning combines multiple transport modes into an integrated transport solution. As such, the environmental advantages of sustainable alternatives are combined with the flexibility and speed of road transport. The key feature of synchromodality is that transport mode decisions are based on real-time information. In this dissertation, we study synchromodal transport planning by developing decision-support models that make use of real-time information. We first investigate synchromodal planning based on real-time updates on stochastic travel times. Transport mode decisions are adapted to the actual travel time duration, which facilitates dealing with delays. Our findings show that synchromodal planning improves the reliability of transport solutions that include sustainable transport modes. Next, we consider synchromodal decision-making from a supply chain perspective by integrating transport decisions with inventory management. Specifically, transport mode decisions are coordinated with the real-time urgency of the inventory replenishment. We find that postponing transport mode decisions, and adjusting them to real-time inventory information, enables a modal shift while maintaining service levels. The developed synchromodal planning models approach the modal shift from a cost-minimization perspective. However, optimizing for emission reductions may further improve the shift towards sustainable transport modes. In a final research project, we evaluate the cost-emissions trade-off of a modal shift under different optimization objectives.