In this paper we propose a hybrid motorway travel time prediction framework aimed at providing pre-trip travel information in case of roadworks. The framework utilises a first order macroscopic traffic flow model to predict the consequences in travel time of changes in both traffic demand and roadway capacity. Data-driven approaches are used to estimate both demand and capacity. From a large database of detailed loop detection data the most likely demand profiles are estimated for the considered workzone location, taking into account the possible effects of mobility management. Capacity estimation techniques are employed which combine historical data with likely capacity reduction factors as a consequence of the roadworks. On the basis of evaluation on a workzone case on a densely used 20 km stretch of a three lane motorway in The Netherlands, it is demonstrated that the proposed approach is able to predict travel time within a 20% margin in over 96% of all cases. The proposed approach is suitable for trip planning purposes (e.g. route planners and travel information websites), and may also be utilised by the road authorities in the actual planning of the roadworks.