Recent advancements in machine learning, and neural networks in particular, have introduced new opportunities for activity-based travel demand modeling and scheduling, providing data-driven alternatives to traditional theory-driven methods. While previous machine learning-based scheduling models have integrated combinations of activity, destination, and mode choice as separate sub-models, none have yet, to the best of our knowledge, unified these components into a single, jointly learned framework.This paper introduces Skyline-NNjoint, a novel fully neural network-based scheduling model that jointly predicts an agent’s activity, destination, and mode choice decisions at each discrete time step throughout the day. To capture substitution effects and interdependencies among alternatives, the model introduces a Global Context Module (GCM) that enables each alternative to adjust its attractiveness based on the context of all others. While similar context-based approaches have been used in other domains, this is, to the best of our knowledge, the first application of such a mechanism in travel demand modeling. This provides a data-driven approach to relax the Independence of Irrelevant Alternatives (IIA) assumption inherent in multinomial logit models. The effectiveness of the GCM is evaluated by comparing Skyline-NNjoint to a baseline version without it, isolating its contribution to model performance.The model is trained on travel survey data from Stockholm and evaluated using both cross-entropy loss and simulated daily activity–travel trajectories. Cross-entropy loss results confirm that the GCM improves predictive performance. Simulation results show that Skyline-NNjoint produces patterns of activity participation, trip timing, and mode choice that closely match observed data. Notably, the model accurately reproduces mode distributions across activity purposes, highlighting its capacity to capture interdependencies in joint decision-making.