Itinerary recommendation involves suggesting a sequence of Points of Interests (POIs) that users obtain maximum satisfaction under a time budget. Existing models have three challenges. First, they model user interest as non-time dependent, which cannot capture user interest appropriately because user interest can be contextual on time, e.g., interest in restaurants are likely higher during typical meal times. Second, they model the distance dependency of user interest as a linear one, which does not always adequately capture this relationship, e.g., it could be a cubic decay relationship. Finally, existing studies treat POI recommendation and itinerary optimisation as two separate problems, which can result in sub-optimal itinerary recommendations. In this paper, we propose a deep learning model that recommends POIs and constructs the itinerary simultaneously and in an integrated manner. It captures user dynamic interest and non-linear spatial dependencies in itinerary recommendations. The proposed model has two steps, where the candidate selection policy generates a set of personalised candidate POIs based on user interest and the itinerary construction step maximises user interest within budget time. To recommend an appropriate candidate set, we propose a multi-head, attention-based transformer to leverage periodic trends and recent activities to capture user dynamic preferences. We also introduce a new co-visiting patterns-based graph convolutional network (GCN) model to capture user non-linear spatial dependencies. To construct the full itinerary from the dynamic candidate sets, we apply greedy policy that incrementally constructs itineraries within the budget time which aims to maximise user interest and minimize queuing time. Experimental results show that the proposed deep learning model outperforms state-of-the-art baselines in itinerary recommendation in four theme parks and four cities datasets. The proposed model outperforms the baselines in itinerary recommendation from 7.79% to 26.28% on various datasets in terms of F1-score value. We also show that the proposed candidate generation approach outperforms the state-of-the-art next POI recommendation models in eight real datasets. The proposed model outperforms the baselines on average by 11.29 % in terms of F1-score@5 values and 9.08% in terms of F1-score@10 values. We have publicly shared our source code at GitHub 1 for the reproducibility of our proposed model.