MotiveMob：将动机作为语义动作的闭环人类移动生成模型

中文摘要 AI 辅助

人类移动生成是城市研究中的重要任务，用于合成轨迹数据以支撑城市规划与交通管理。人类移动可被描述为“为什么-在哪里-何时”的决策过程：人们先形成移动意图，再确定对应活动的地点与时间。在用户层面和时间分布偏移下的轨迹生成，或可通过显式建模该决策结构实现性能提升。然而，现有多数人类移动生成方法要么以粗粒度方式表示行为意图（如每日计划或轨迹级描述），要么直接预测未来位置，未对每一步移动的可能动机进行显式推理。本文提出MotiveMob，一种动机驱动的自回归人类移动生成框架，该框架先对下一步移动的可能原因做出假设，再联合生成其地点与时间。在每一步，动机预测器以当前移动状态、长期行为报告和移动历史为条件，推断合理动机或判断轨迹是否应终止；在得到假设动机后，状态预测器将其落地为候选下一个位置与到达时间，该候选需经过速度可行性与重复性检查后，反馈至下一步决策。我们在涉及未见过的用户和未见过的时间周期（包括季节变化及COVID-19大流行导致的重大行为中断）的分布偏移场景下评估MotiveMob，实验表明，在用户层面和时间分布偏移下，MotiveMob相比基于预训练和提示的竞争方法，始终实现更优的分布保真度，展现出对分布外移动模式的稳健泛化能力。

英文摘要

Human mobility generation, an important task in urban research, synthesizes trajectory data for urban planning and transportation management. Human mobility can be characterized as a "why-where-when" decision process: people form an intention to move and then determine where and when the corresponding activity will take place. Trajectory generation under user-level and temporal distribution shifts may benefit from explicitly modeling this decision structure. However, many existing human mobility generation methods either represent behavioral intent at a coarse granularity, such as a daily plan or a trajectory-level description, or directly predict future locations without explicitly reasoning about a possible motivation for each movement step. We introduce MotiveMob, a motivation-driven autoregressive framework for human mobility generation that first forms a hypothesis about why the next movement may occur and then jointly generates where and when it may occur. At each step, a motivation predictor conditions on the current mobility state, a long-term behavioral report, and the mobility history to infer a plausible motivation or determine whether the trajectory should terminate. Given the hypothesized motivation, a state predictor grounds it in a candidate next location and arrival time. The candidate then undergoes speed-feasibility and repetition checks before being fed back for the next decision. We evaluate MotiveMob under distribution shifts involving unseen users and unseen temporal periods, including seasonal changes and the substantial behavioral disruption caused by the COVID-19 pandemic. Experiments show that MotiveMob consistently achieves better distributional fidelity than competitive pretraining-based and prompting-based methods under user-level and temporal distribution shifts, demonstrating robust generalization to out-of-distribution mobility patterns.

发表机构

• The University of Osaka（大阪大学）
• The University of Tokyo（东京大学）
• The Hong Kong University of Science and Technology（香港科技大学）
• Institute of Science Tokyo（东京科学大学）

机构由 AI 辅助整理，请以论文原文为准。