STUaNet: Understanding uncertainty in spatiotemporal collective human mobility

TL;DR

STUaNet introduces a novel framework for uncertainty quantification in spatiotemporal human mobility forecasting, combining hierarchical data turbulence and adaptive re-calibration to improve prediction accuracy and uncertainty estimation.

Contribution

The paper presents a new uncertainty learning mechanism with hierarchical data turbulence and a gated re-calibration method for better spatiotemporal forecasting.

Findings

Outperforms existing models in mobility forecasting accuracy.

Effectively quantifies both data quality and external uncertainty.

Demonstrates robustness across three real-world datasets.

Abstract

The high dynamics and heterogeneous interactions in the complicated urban systems have raised the issue of uncertainty quantification in spatiotemporal human mobility, to support critical decision-makings in risk-aware web applications such as urban event prediction where fluctuations are of significant interests. Given the fact that uncertainty quantifies the potential variations around prediction results, traditional learning schemes always lack uncertainty labels, and conventional uncertainty quantification approaches mostly rely upon statistical estimations with Bayesian Neural Networks or ensemble methods. However, they have never involved any spatiotemporal evolution of uncertainties under various contexts, and also have kept suffering from the poor efficiency of statistical uncertainty estimation while training models with multiple times. To provide high-quality uncertainty quantification for spatiotemporal forecasting, we propose an uncertainty learning mechanism to simultaneously estimate internal data quality and quantify external uncertainty regarding various contextual interactions. To address the issue of lacking labels of uncertainty, we propose a hierarchical data turbulence scheme where we can actively inject controllable uncertainty for guidance, and hence provide insights to both uncertainty quantification and weak supervised learning. Finally, we re-calibrate and boost the prediction performance by devising a gated-based bridge to adaptively leverage the learned uncertainty into predictions. Extensive experiments on three real-world spatiotemporal mobility sets have corroborated the superiority of our proposed model in terms of both forecasting and uncertainty quantification.