This paper addresses adjusted travel route choice in the context of new transport developments and incremental traveller learning. It is assumed that new developments can impact traveller perceptions and adjustments in multiple ways. For instance, if travellers expect a project to significantly increase or decrease overall travel demand they may change their daily route choice based on those new expectations. Further, over time, travellers will learn actual network demand, and adapt their route choice accordingly. In particular, this paper employs a methodological framework to model the day-to-day learning process of road users, and the corresponding system performance over time with a focus on the impact of specific new developments. Travellers assume an initial demand distribution, and incrementally update it based on their day-to-day travel experiences. Bayesian Inference is used to update the travel demand distribution, and the strategic user equilibrium model is used to compute the underlying traffic assignment pattern. Numerical analysis is conducted on a test network to demonstrate the learning process in terms of the perceived travel demand, path choice, and perceived path travel times.