Service reliability is one of the main factors influencing public transport level of service and, thus, passengers’ satisfaction. Public transport services are subject to various sources of uncertainty related to traffic conditions, public transport operations and passenger demand. Passengers are able to form their perception of trip attributes and service reliability through accumulating experiences of repetitive travel choices. Perceived service reliability can be improved either by increasing the ground-truth service reliability (e.g. introduce exclusive bus lanes, control strategies etc.) or by providing real time information (RTI) to passengers. However, RTI prediction schemes might not be perfectly accurate and thus, passengers might be able to account for the reliability of the provided information as well. The learning mechanism of individuals becomes, as a result, an important component in Dynamic Transit Assignment Models (DTAM) which enables accounting for how perceived reliability of service and the provided information evolves, through iterative network loading. This thesis provided the modeling framework for passengers’ perception of reliability and its effects on decision making with respect to path choice. Within-day effect is represented through the incorporation of scheduling constraints, while passengers’ learning mechanism accounts for updates in their expectations and the perceived level of information credibility in the day-to-day context. The proposed model was applied to Stockholm’s rapid transit network which was simulated in BusMezzo, an agent-based public transport assignment model. The application used the real-world timetables, vehicle schedules and RTI prediction scheme. Passengers’ learning function was analysed under various specifications which corresponded to different levels of adaptation. The results highlight the importance of capturing service uncertainty and the credibility associated with alternative information sources, while they stress the need for empirical estimation and validation of the proposed model. This study also provides the framework for future evaluation of measures which aim to improve service reliability.