Plan Globally, Act Locally: Adaptive Segment Forecasting for Long-Horizon Human Mobility

Long-horizon human mobility forecasting must predict a user's future movements over multiple days without accumulating errors from repeated short-horizon rollouts. Human mobility has a natural hierarchy: people make high-level plans across days, while their movements appear as fine-grained location choices over time. Following the principle of planning globally and acting locally, we propose PlanCAD (Plan–Critic Adaptive Diffusion) for long-horizon human mobility forecasting. PlanCAD uses daily plan tokens to capture long-horizon structure and segment-level generation to produce slot-level location predictions. Specifically, PlanCAD uses a large language model (LLM)-based mobility backbone to encode historical trajectories into day-level representations, and a learnable planner generates future daily plan tokens from these representations. A critic scores candidate execution horizons using plan-level and trajectory-level statistics, deciding whether to predict a longer segment or replan earlier. Given the selected plan, a diffusion actor refines future mobility representations in the LLM hidden space and maps them to location predictions. Experiments on real-world mobility datasets show that PlanCAD improves 7-to-7 Acc@3 on Kumamoto from 50.09% to 51.10% over the strongest baseline, with consistent gains on Sapporo and Hiroshima. Ablation studies and analysis further show that complementary critic features support adaptive horizon selection, while plan-conditioned generation improves multi-day forecasting stability.