Recent studies have significantly improved the prediction accuracy of travel demand using graph neural networks. However, these studies largely ignored uncertainty that inevitably exists in travel demand prediction. To fill this gap, this study proposes a framework of probabilistic graph neural networks (Prob-GNN) to quantify the spatiotemporal uncertainty of travel demand. This Prob-GNN framework is substantiated by deterministic and probabilistic assumptions, and empirically applied to the task of predicting the transit and ridesharing demand in Chicago. We found that the probabilistic assumptions (e.g. distribution tail, support) have a greater impact on uncertainty prediction than the deterministic ones (e.g. deep modules, depth). Among the family of Prob-GNNs, the GNNs with truncated Gaussian and Laplace distributions achieve the highest performance in transit and ridesharing data. Even under significant domain shifts, Prob-GNNs can predict the ridership uncertainty in a stable manner, when the models are trained on pre-COVID data and tested across multiple periods during and after the COVID-19 pandemic. Prob-GNNs also reveal the spatiotemporal pattern of uncertainty, which is concentrated on the afternoon peak hours and the areas with large travel volumes. Overall, our findings highlight the importance of incorporating randomness into deep learning for spatiotemporal ridership prediction. Future research should continue to investigate versatile probabilistic assumptions to capture behavioral randomness, and further develop methods to quantify uncertainty to build resilient cities.
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
Florida State University（佛罗里达州立大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Alabama, Tuscaloosa（阿拉巴马大学塔斯卡洛osa分校）；University of Florida（佛罗里达大学）
Massachusetts Institute of Technology（麻省理工学院）；University of Florida（佛罗里达大学）；Northeastern University（东北大学）
Origin-Destination (O-D) travel demand prediction is a fundamental challenge in transportation. Recently, spatial-temporal deep learning models demonstrate the tremendous potential to enhance prediction accuracy. However, few studies tackled the uncertainty and sparsity issues in fine-grained O-D matrices. This presents a serious problem, because a vast number of zeros deviate from the Gaussian assumption underlying the deterministic deep learning models. To address this issue, we design a Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) to quantify the uncert…
Argonne National Laboratory（阿贡国家实验室）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Deep-learning-based data-driven forecasting methods have produced impressive results for traffic forecasting. A major limitation of these methods, however, is that they provide forecasts without estimates of uncertainty, which are critical for real-time deployments. We focus on a diffusion convolutional recurrent neural network (DCRNN), a state-of-the-art method for short-term traffic forecasting. We develop a scalable deep ensemble approach to quantify uncertainties for DCRNN. Our approach uses a scalable Bayesian optimization method to perform hyperparameter optimization, selects a set of hi…
Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimens…
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction
Peking University（北京大学）；Massachusetts Institute of Technology（麻省理工学院）；University College London（伦敦大学学院）；University of Chinese Academy of Sciences（中国科学院大学）；Chengdu University of Technology（成都理工大学）
Huazhong University of Science and Technology（华中科技大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Georgia Institute of Technology（佐治亚理工学院）；Wuhan University（武汉大学）；University of Cambridge（剑桥大学）；University of Luxembourg（卢森堡大学）；Weill Cornell Medical College（威尔康奈尔医学院）
The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery ($\text{S}^3\text{d}$), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning; which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model compl…
The statistical quantification of temperature processes for the analysis of urban heat island (UHI) effects and local heat-waves is an increasingly important application domain in smart city dynamic modelling. This leads to the increased importance of real-time heatwave risk management on a fine-grained spatial resolution. This study attempts to analyze and develop new methods for modelling the spatio-temporal behavior of ground temperatures. The developed models consider higher-order stochastic spatial properties such as skewness and kurtosis, which are key components for understanding and de…
This study evaluates the effectiveness of using Google Maps Location History data to identify joint activities in social networks. To do so, an experiment was conducted where participants were asked to execute daily schedules designed to simulate daily travel incorporating joint activities. For Android devices, detection rates for 4-person group activities ranged from 22% under the strictest spatiotemporal accuracy criteria to 60% under less strict yet still operational criteria. The performance of iPhones was markedly worse than Android devices, irrespective of accuracy criteria. In addition,…
The increasing market penetration of electric vehicles (EVs) may change the travel behavior of drivers and pose a significant electricity demand on the power system. Since the electricity demand depends on the travel behavior of EVs, which are inherently uncertain, the forecasting of daily charging demand (CD) will be a challenging task. In this paper, we use the recorded GPS data of EVs and conventional gasoline-powered vehicles from the same city to investigate the potential shift in the travel behavior of drivers from conventional vehicles to EVs and forecast the spatiotemporal patterns of …
University of Science and Technology of China（中国科学技术大学）；Beijing Institute of Control Engineering（北京控制工程研究所）
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…
University of Bath（巴斯大学）；Imago7 foundation（Imago7基金会）；University of Edinburgh（爱丁堡大学）；GE Healthcare（GE医疗）；Technical University of Munich（慕尼黑工业大学）
We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage algorithm. This minimises the under-sampling (aliasing) artefacts from aggressively short scan times. The learned quantitative inference phase is purely trained on physical simulations (Bloch equations) that are flexible for producing rich training sa…
Decision-Oriented Uncertainty Quantification for Risk Control in Earth System Spatiotemporal Foundation Models
Vanderbilt University（范德堡大学）；City University of New York（纽约城市大学）；Yale University（耶鲁大学）；Wyze Inc.（Wyze公司）；Northeastern University（东北大学）；University of Southern California（南加州大学）；Southwest University（西南大学）
Accurately predicting when and where ambulance call-outs occur can reduce response times and ensure the patient receives urgent care sooner. Here we present a novel method for ambulance demand prediction using Gaussian process regression (GPR) in time and geographic space. The method exhibits superior accuracy to MEDIC, a method which has been used in industry. The use of GPR has additional benefits such as the quantification of uncertainty with each prediction, the choice of kernel functions to encode prior knowledge and the ability to capture spatial correlation. Measures to increase the uti…
Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework
UC San Diego（加州大学圣地亚哥分校）；University of Washington（华盛顿大学）；Northeastern University（东北大学）
Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. In high stakes domains, being able to generate probabilistic forecasts with confidence intervals is critical to risk assessment and decision making. Hence, a systematic study of uncertainty quantification (UQ) methods for spatiotemporal forecasting is missing in the community. In this paper, we describe two types of spatiotemporal forecasting problems: regular grid-based and graph-based. Then we analyz…
What do data tell us about physics-and what don't they tell us? There has been a surge of interest in using machine learning models to discover governing physical laws such as differential equations from data, but current methods lack uncertainty quantification to communicate their credibility. This work addresses this shortcoming from a Bayesian perspective. We introduce a novel model comprising "leaf" modules that learn to represent distinct experiments' spatiotemporal functional data as neural networks and a single "root" module that expresses a nonparametric distribution over their governi…
Image segmentation with traveling waves in an exactly solvable recurrent neural network
Western University（韦仕敦大学）；Western Institute for Neuroscience（韦仕敦神经科学研究所）；Western Academy for Advanced Research（韦仕敦高等研究院）；University of Amsterdam（阿姆斯特丹大学）
STRelay: A Universal Spatio-Temporal Relaying Framework for Location Prediction over Human Trajectory Data
State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau（物联网智能城市国家重点实验室和计算机与信息科学系，澳门大学）；Beijing Normal University-Hong Kong Baptist University United International College（北京师范大学-香港 Baptist大学联合国际学院）
University of Science and Technology of China（中国科学技术大学）；Huawei Technologies（华为技术有限公司）
Spatiotemporal forecasting is an imperative topic in data science due to its diverse and critical applications in smart cities. Existing works mostly perform consecutive predictions of following steps with observations completely and continuously obtained, where nearest observations can be exploited as key knowledge for instantaneous status estimation. However, the practical issues of early activity planning and sensor failures elicit a brand-new task, i.e., non-consecutive forecasting. In this paper, we define spatiotemporal learning systems with missing observation as Grey Spatiotemporal Sys…
Tackling Incomplete Data in Air Quality Prediction: A Bayesian Deep Learning Framework for Uncertainty Quantification
School of Intelligent Systems Engineering, Sun Yat-sen University（智能系统工程学院，中山大学）；Guangdong Provincial Key Laboratory of Intelligent Transportation System（广东省智能交通系统重点实验室）；Guangdong Provincial Engineering Research Center for Traffic Environmental Monitoring and Control（广东省交通环境监测与控制工程研究中心）
Link Representation Learning for Probabilistic Travel Time Estimation
National Engineering Research Center of Mobile Network Technologies, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（中国移动网络技术国家工程研究中心，信息与通信工程学院，北京邮电大学）；Department of Civil Engineering, McGill University（土木工程系，麦吉尔大学）
Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments, where the network trained for one set of imaging conditions becomes sub-optimal for different ones. This limitation is particularly stringent in the quest to have an automated experiment setting, where retraining or transfer learning becomes impr…
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
Data and Artificial Intelligence Cluster, Eindhoven University of Technology（埃因霍温理工大学数据与人工智能集群）；Swiss Plasma Center, École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院瑞士等离子体中心）
High-Rate Phase Association with Travel Time Neural Fields
University of Basel（巴塞尔大学）；La Sapienza Università di Roma（罗马大学（萨皮恩扎））；Pennsylvania State University（宾夕法尼亚州立大学）；Rice University（莱斯大学）；University of Illinois at Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校）
