# GTG: Generalizable Trajectory Generation Model for Urban Mobility

arXiv: https://arxiv.org/abs/2502.01107 · HTML: https://mlsanity.com/paper/2502.01107/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2025.

## Built on by (most cited first, 6 of 6)

- [Multi-Agent Pointer Transformer: Seq-to-Seq Reinforcement Learning for Multi-Vehicle Dynamic Pickup-Delivery Problems](https://mlsanity.com/paper/2511.17435/) (2025)
- [HieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecasting](https://mlsanity.com/paper/2512.07854/) (2025)
- [Dynamic Population Distribution Aware Human Trajectory Generation with Diffusion Model](https://mlsanity.com/paper/2511.01929/) (2025)
- [RoSA: Enhancing Parameter-Efficient Fine-Tuning via RoPE-aware Selective Adaptation in Large Language Models](https://mlsanity.com/paper/2511.21733/) (2025)
- [From GPS Points to Travel Patterns: Flexible and Semantic Trajectory Generation with LLMs](https://mlsanity.com/paper/2605.30014/) (2026)
- [Relative Transitions, Not Absolute Destinations: A Transfer-and-Ground Framework for Target-Trajectory-Free Human Mobility Generation](https://mlsanity.com/paper/2610.02033/) (2026)

## Abstract (arXiv)

Trajectory data mining is crucial for smart city management. However, collecting large-scale trajectory datasets is challenging due to factors such as commercial conflicts and privacy regulations. Therefore, we urgently need trajectory generation techniques to address this issue. Existing trajectory generation methods rely on the global road network structure of cities. When the road network structure changes, these methods are often not transferable to other cities. In fact, there exist invariant mobility patterns between different cities: 1) People prefer paths with the minimal travel cost; 2) The travel cost of roads has an invariant relationship with the topological features of the road network. Based on the above insight, this paper proposes a Generalizable Trajectory Generation model (GTG). The model consists of three parts: 1) Extracting city-invariant road representation based on Space Syntax method; 2) Cross-city travel cost prediction through disentangled adversarial training; 3) Travel preference learning by shortest path search and preference update. By learning invariant movement patterns, the model is capable of generating trajectories in new cities. Experiments on three datasets demonstrates that our model significantly outperforms existing models in terms of generalization ability.