Looking Ahead and Back: Anticipatory Generative Modeling for Road-Network Trajectories

Synthetic trajectory generation underpins urban applications such as traffic management and urban planning. In the real world, vehicles are constrained by the physical boundaries of the road network and make sequential routing decisions at intersections navigating toward their destinations. However, state-of-the-art methods primarily focus on simulating location sequences, often producing trajectories that drift off the road network and deviate substantially from the realistic. In this paper, we advocate a fundamental paradigm shift: moving beyond mere location simulation toward route planning on the road network. It is particularly challenging at intersections, where multiple outgoing transitions may appear equally plausible given the observed history yet lead to markedly different downstream routes. To address this challenge, we revisit stochastic dynamics on graphs and introduce a new graph-based generative paradigm—Anticipatory Generation—which resolves such decision ambiguity by constructing a short-horizon trajectory continuation (anticipation) before committing to an action prediction. Specifically, we present an anticipatory Foresight model that generates plausible trajectories through progressive action prediction on the road network. In Foresight, we formulate a probabilistic model to incorporate anticipation, construct fiber bundles for the transitions reconciling road topologies, and embed recurrent anticipation updates into the generation process. Extensive experiments on real-world datasets demonstrate that Foresight wins state-of-the-art baselines across diverse metrics. Codes are available at https://....