MotiveMob improves human mobility prediction under changing conditions
MotiveMob: Motivation as Semantic Action for Closed-Loop Human Mobility Generation
Machine Learning
Summary
Predicting where and when people will go around a city helps urban planners and transportation managers. Many current methods guess locations directly but don’t clearly think about the reasons why people move. The authors created MotiveMob, a system that first guesses why a person moves, then uses that to predict where and when they go next. This approach works better even when people’s behaviors change a lot, like during seasonal shifts or the COVID-19 pandemic.
What this means in practice
- •For urban planners: Generate realistic future human movement patterns that adapt to changing behaviors for better city and transport planning.
- •For smart city developers: Create adaptive mobility simulations that account for unexpected disruptions such as pandemics or seasonal changes.
Authors
Mengkun Gao, Zengqing Wu, Renhe Jiang, Jiawei Wang, Yusong Wang, Chuang Yang, Shuyuan Zheng, Makoto Onizuka, Chuan Xiao
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.