Abstract: Disclosed herein is a method for generating a model that can be used by a routing module to determine estimated travel times for routes within a navigable network. The model is generated by a process of machine learning with training data that is obtained from historic positional data recorded by devices travelling within the navigable network. The model can be updated over time as new positional data is obtained. The newly obtained positional data is also used for evaluating the performance of the model and for detecting so-called ‘pattern-breaking’ events where the underlying conditions in the navigable network suddenly change such that the previous version of the model may no longer give accurate estimated travel times.