arXiv：cs.LG· Mengkun Gao, Zengqing Wu, Renhe Jiang, Jiawei Wang, Yusong Wang, Chuang Yang, Shuyuan Zheng, Makoto Onizuka, Chuan Xiao·· 3 小时前
MotiveMob：以动机为语义动作的闭环人类移动轨迹生成框架
MotiveMob: Motivation as Semantic Action for Closed-Loop Human Mobility Generation
AI 导读
研究者提出 MotiveMob，一个动机驱动的自回归人类移动轨迹生成框架：每步先假设"为何移动"，再联合生成"去向何处、何时到达"，候选位置需通过速度可行性与重复性检查后反馈进入下一步决策。在未见用户与未见时段（含季节变化及 COVID-19 行为扰动）的分布偏移下，MotiveMob 的分布保真度持续优于基于预训练和基于提示词的方法。
正文
Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11442 [cs.LG] |
| (or arXiv:2610.11442v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11442 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mengkun Gao [view email]
[v1]
Thu, 8 Oct 2026 08:00:52 UTC (7,295 KB)
来源：arXiv：cs.LG · arxiv.org