Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasaurus

TL;DR

This paper introduces MIRAGE, a neural trajectory generator that imitates human decision-making to produce realistic synthetic trajectories, and proposes a comprehensive benchmarking protocol to evaluate their utility beyond traditional statistical measures.

Contribution

MIRAGE is a novel human-imitative trajectory generation model that avoids Datasaurus biases and a new evaluation framework for downstream task performance.

Findings

MIRAGE outperforms baselines in statistical similarity by 59-68%.

MIRAGE improves task performance metrics by 11-33%.

Ablation studies confirm the effectiveness of MIRAGE's design choices.

Abstract

Human trajectory data, which plays a crucial role in various applications such as crowd management and epidemic prevention, is challenging to obtain due to practical constraints and privacy concerns. In this context, synthetic human trajectory data is generated to simulate as close as possible to real-world human trajectories, often under summary statistics and distributional similarities. However, these similarities oversimplify complex human mobility patterns (a.k.a. ``Datasaurus''), resulting in intrinsic biases in both generative model design and benchmarks of the generated trajectories. Against this background, we propose MIRAGE, a huMan-Imitative tRAjectory GenErative model designed as a neural Temporal Point Process integrating an Exploration and Preferential Return model. It imitates the human decision-making process in trajectory generation, rather than fitting any specific statistical distributions as traditional methods do, thus avoiding the Datasaurus issue. We also propose a comprehensive task-based evaluation protocol beyond Datasaurus to systematically benchmark trajectory generative models on four typical downstream tasks, integrating multiple techniques and evaluation metrics for each task, to assess the ultimate utility of the generated trajectories. We conduct a thorough evaluation of MIRAGE on three real-world user trajectory datasets against a sizeable collection of baselines. Results show that compared to the best baselines, MIRAGE-generated trajectory data not only achieves the best statistical and distributional similarities with 59.0-67.7% improvement, but also yields the best performance in the task-based evaluation with 10.9-33.4% improvement. A series of ablation studies also validate the key design choices of MIRAGE.