MotiveMob: Motivation as Semantic Action for Closed-Loop Human Mobility Generation
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.
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Key points
- 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.
- 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.
Sources (1)
- [1]MotiveMob: Motivation as Semantic Action for Closed-Loop Human Mobility GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 08:00 AM
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.
Human mobility generation, an important task in urban research, synthesizes trajectory data for urban planning and transportation management.
Extractive summary: sentences quoted from the sources.