2021/02/09 by Zhengyang Zhou, Yang Wang, Xike Xie +2 · 19 citations
Computer Science · Engineering · Mathematics · Social Sciences · #Artificial intelligence #Bayesian probability #Computer science #Data mining #Human Mobility and Location-Based Analysis #Leverage (statistics) #Machine learning #Probabilistic logic #Propagation of uncertainty #Sensitivity analysis #Simulation #Traffic Prediction and Management Techniques #Transportation Planning and Optimization #Uncertainty analysis #Uncertainty quantification #cs.LG #cs.NA #math.NA
paper · pdf · open access · doi:10.1145/3442381.3449817
12 pages, 8 figures, WWW 2021 Conference
arxiv created 2021/02/09 · arxiv updated 2021/02/12 · openalex created_date 2021/02/15 · openalex publication_date 2021/04/19 · openalex updated_date 2026/08/05
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.