The relationship between travel growth, increased congestion and effectiveness of traffic management measures can be better understood by examination of change in people’s travel patterns due to congestion and its mitigation policies. The studies suggested that combined models are vital to accurately foresee the impact of policies on travel behaviour, as they integrate the effect of congestion on the scheduling of activities through feedback mechanism. Models within the Activity-based approach predict an individual activity-agenda and its schedule but they lack in representing congestion as an endogenous variable. In contrast, combined models are limited as they tend to incorporate fewer scheduling dimensions for a part of the activity-travel pattern (e.g. home to work trip). Based on this, the primary objective of this thesis is to contribute towards improvements and extensions of the existing combined models. This thesis presented a combined model that integrates the modelling of activity scheduling dimensions (for daily and weekly activity-travel patterns) with the dynamic representation of congestion under the framework of the fixed point problem. Modelled scheduling dimensions include: departure time, activity duration, activity sequence and route choice. The essential aspect of the model is based on the trade-off between the utility of participating in various activities, which contain time-of-day preference and satiation effects, and the disutility of travel. The development process presented for the model is generalised and it can accommodate any operational model within the demand and supply sides. However, the model application in this thesis is limited to the simplified network which can be extended for a real network by following the notions of model development. A variety of numerical experiments were performed in order to assess the model working and the implications of a range of policies. Results obtained from all the numerical experiments are plausible and these are explained well in the thesis.