GTG: Generalizable Trajectory Generation Model for Urban Mobility

TL;DR

This paper introduces GTG, a trajectory generation model that captures invariant mobility patterns across cities, enabling accurate trajectory synthesis in new urban environments despite changes in road network structures.

Contribution

The paper presents a novel, generalizable trajectory generation model that leverages invariant mobility patterns and cross-city learning to improve trajectory prediction in diverse urban settings.

Findings

GTG outperforms existing models in cross-city trajectory generation.

The model effectively captures invariant mobility patterns.

Experiments demonstrate improved generalization in three datasets.

Abstract

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.