Foundation Model for Conditional Trajectory Generation with Unstable Context Availability

With the proliferation of data-driven smart city applications, massive amounts of trajectory data are being collected. These trajectories are typically captured as sequences of GPS coordinates, and are often enriched with diverse contextual information, such as timestamps, user IDs, and service metadata. They form the backbone of many smart city applications such as navigation, logistics, and urban planning. Generative tasks on this data - such as trajectory prediction, recovery, and synthesis - play a critical role in these applications by filling data gaps, simulating scenarios, or augmenting datasets, which all require generating coordinates conditioned on partial observations and auxiliary context. There are two weaknesses in most existing methods: i) they are typically tailored for a specific task and rely on fixed sets of contextual features, leading to repeated efforts across tasks and domains; ii) they are not robust to unstable context availability, as contextual information is often incomplete, unavailable, or inconsistent in real-world settings. In this work, we present TrajWeaver , a novel diffusion-based foundation model designed to support multiple conditional trajectory generation tasks within a single framework, while being flexible to diverse conetxts and robust to unstable context availability. TrajWeaver introduces a novel State Propagation Pipeline (SPP) on top of the diffusion model, which uses multi-scale state features as messengers to proprgate and refine contextual information and knowledge across denoising steps. This design improves robustness to missing or partial context and enables seamless adaptation to new types of conditional features without retraining the full model. Experiments across three trajectory generation tasks demonstrate that TrajWeaver achieves strong performance and generalization under different settings, showcasing its potential as a foundation model for trajectory generation.