Effective management of urban traffic is important for any smart city initiative. Therefore, the quality of the sensory traffic data is of paramount importance. However, like any sensory data, urban traffic data are prone to imperfections leading to missing measurements. In this paper, we focus on inter-region traffic data completion. We model the inter-region traffic as a spatiotemporal tensor that suffers from missing measurements. To recover the missing data, we propose an enhanced CANDECOMP/PARAFAC (CP) completion approach that considers the urban and temporal aspects of the traffic. To derive the urban characteristics, we divide the area of study into regions. Then, for each region, we compute urban feature vectors inspired from biodiversity which are used to compute the urban similarity matrix. To mine the temporal aspect, we first conduct an entropy analysis to determine the most regular time-series. Then, we conduct a joint Fourier and correlation analysis to compute its periodicity and construct the temporal matrix. Both urban and temporal matrices are fed into a modified CP-completion objective function. To solve this objective, we propose an alternating least square approach that operates on the vectorized version of the inputs. We conduct comprehensive comparative study with two evaluation scenarios. In the first one, we simulate random missing values. In the second scenario, we simulate missing values at a given area and time duration. Our results demonstrate that our approach provides effective recovering performance reaching 26% improvement compared to state-of-art CP approaches and 35% compared to state-of-art generative model-based approaches.
Beihang University（北京航空航天大学）；University of Central Florida（中佛罗里达大学）
Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow modeling, and climate forecasting. However, the originally collected spatiotemporal data in real-world scenarios is usually incomplete due to sensor failures or transmission loss. Spatiotemporal imputation aims to fill the missing values according to the observed values and the underlying spatiotemporal dependence of them. The previous dominant models impute missing values autoregressively and suffer from the problem of error accumulation. As emerging powerful generative models, the diffusion probabilistic…
MNT-TNN: Spatiotemporal Traffic Data Imputation via Compact Multimode Nonlinear Transform-based Tensor Nuclear Norm
Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences（智能机器研究所，合肥物理科学研究院，中国科学院）；University of Science and Technology of China（中国科学技术大学）
Tsinghua University（清华大学）；Central South University（中南大学）
Large-scale data missing is a challenging problem in Intelligent Transportation Systems (ITS). Many studies have been carried out to impute large-scale traffic data by considering their spatiotemporal correlations at a network level. In existing traffic data imputations, however, rich semantic information of a road network has been largely ignored when capturing network-wide spatiotemporal correlations. This study proposes a Graph Transformer for Traffic Data Imputation (GT-TDI) model to impute large-scale traffic data with spatiotemporal semantic understanding of a road network. Specifically,…
A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation
School of Computer Science, University of Technology Sydney（悉尼科技大学计算机科学学院）；School of Computer Science and Technology, Zhejiang Normal University（浙江师范大学计算机科学与技术学院）；Department of Computer Science and Engineering, University of California, San Diego（加利福尼亚大学圣地亚哥分校计算机科学与工程学院）
Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems. Making accurate imputation is critical to many applications in intelligent transportation systems. In this paper, we formulate the missing data imputation problem in spatiotemporal traffic data in a low-rank tensor completion (LRTC) framework and define a novel truncated nuclear norm (TNN) on traffic tensors of location$\times$day$\times$time of day. In particular, we introduce an universal rate parameter to control the degree of truncation on all tensor modes in the propos…
Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion
School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；School of Software, Tiangong University（天津理工软件学院）；University of Southern Queensland（南方昆士兰大学）
How many stations are sufficient? Exploring the effect of urban weather station density reduction on imputation accuracy of air temperature and humidity
Biometry and Environmental System Analysis, University of Freiburg, Germany（弗赖堡大学生物计量与环境系统分析系）；Chair of Environmental Meteorology, University of Freiburg, Germany（弗赖堡大学环境气象学教授职位）
Missing value problem in spatiotemporal traffic data has long been a challenging topic, in particular for large-scale and high-dimensional data with complex missing mechanisms and diverse degrees of missingness. Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data. However, despite the promising results, these approaches do not scale well to large data tensors. In this paper, we focus on addressing the missing data imputation problem for lar…
Collaborative Imputation of Urban Time Series through Cross-city Meta-learning
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University（香港理工大学土木与环境工程系）；Department of Traffic Engineering, Tongji University（同济大学交通工程系）；Centre for Learning, Teaching, and Technology, The Education University of Hong Kong（香港教育大学学习、教学与科技中心）；Research Institute for Artificial Intelligence of Things, Department of Computing, The Hong Kong Polytechnic University（香港理工大学人工智能物联网研究所）
The University of Hong Kong（香港大学）；McGill University（麦吉尔大学）
Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent transportation systems. Despite extensive research regarding traffic data imputation, there still exist two limitations to be addressed: first, existing approaches fail to capture the complex spatiotemporal dependencies in traffic data, especially the dynamic spatial dependencies evolving with time; second, prior studies mainly focus on randomly missing patterns while other more complex missing scenarios are less discussed. To fill these research gaps, we propose a novel deep learning framework call…
Rapid advances in sensor, wireless communication, cloud computing and data science have brought unprecedented amount of data to assist transportation engineers and researchers in making better decisions. However, traffic data in reality often has corrupted or incomplete values due to detector and communication malfunctions. Data imputation is thus required to ensure the effectiveness of downstream data-driven applications. To this end, numerous tensor-based methods treating the imputation problem as the low-rank tensor completion (LRTC) have been attempted in previous works. To tackle rank min…
Spatiotemporal traffic time series (e.g., traffic volume/speed) collected from sensing systems are often incomplete with considerable corruption and large amounts of missing values, preventing users from harnessing the full power of the data. Missing data imputation has been a long-standing research topic and critical application for real-world intelligent transportation systems. A widely applied imputation method is low-rank matrix/tensor completion; however, the low-rank assumption only preserves the global structure while ignores the strong local consistency in spatiotemporal data. In this …
Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems. Deep autoregressive models suffer from error propagation which becomes catastrophic for imputing long-range sequences. In this paper, we take a non-autoregressive approach and propose a novel deep generative model: Non-AutOregressive Multiresolution Imputation (NAOMI) to impute long-range sequences given arbitrary missing patterns. NAOMI exploits the multiresolution structure of spatiotemporal data and decodes recursively from coarse to fine-grained resolution…
This paper describes CRNNs we used to participate in Task 5 of the DCASE 2020 challenge. This task focuses on hierarchical multilabel urban sound tagging with spatiotemporal context. The code is available on our GitHub repository at https://github.com/multitel-ai/urban-sound-tagging.
Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study
Massachusetts Institute of Technology（麻省理工学院）；Singapore–MIT Alliance for Research and Technology Centre (SMART)（新加坡-麻省理工研究与技术联盟中心）；University of Florida（佛罗里达大学）
UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Alibaba Cloud Intelligence Group（阿里云智能事业群）
New York University（纽约大学）；Cornell Lab of Ornithology（康奈尔鸟类学实验室）；Adobe Research（奥多比研究院）
We present SONYC-UST-V2, a dataset for urban sound tagging with spatiotemporal information. This dataset is aimed for the development and evaluation of machine listening systems for real-world urban noise monitoring. While datasets of urban recordings are available, this dataset provides the opportunity to investigate how spatiotemporal metadata can aid in the prediction of urban sound tags. SONYC-UST-V2 consists of 18510 audio recordings from the "Sounds of New York City" (SONYC) acoustic sensor network, including the timestamp of audio acquisition and location of the sensor. The dataset cont…
University of Houston（休斯顿大学）；Oak Ridge Associated Universities（橡树岭联合大学）
Satellite-derived measurements are negatively impacted by cloud cover and surface reflectivity. These biases must be discarded and significantly increase the amount of missing data within remote sensing images. This paper expands the application of a partial convolutional neural network (PCNN) to incorporate depthwise convolution layers, conferring temporal dimensionality to the imputation process. The addition of a temporal dimension to the imputation process adds a state of successive existence within the dataset which spatial imputation cannot capture. The depthwise convolution process enab…
A Dataset and Model for Imputing Water Surface Elevation on a Large and Extremely Sparse Spatiotemporal Graph
European Space Agency, Science Hub（欧洲空间局科学中心）；University of Florence（佛罗伦萨大学）；Imperial College London（伦敦帝国理工学院）；Université Bretagne Sud, IRISA（南布列塔尼大学，IRISA）；University of Tromsø（特罗姆瑟大学）；Serco Italia SpA（意大利Serco股份公司）
UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services
Hong Kong University of Science and Technology, Hong Kong（香港科技大学）；Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen（厦门福建省智慧城市感知与计算重点实验室）；School of Informatics, Xiamen University, Xiamen（厦门大学信息学院）；School of Computer Science, Peking University, Beijing（北京大学计算机学院）
ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, USTC（中国科学技术大学苏州高等研究院）；Hong Kong University of Science and Technology（香港科技大学）；JD Intelligent Cities Research（京东智慧城市研究院）
Spatiotemporal downscaling and nowcasting of urban land surface temperatures with deep neural networks
Department of Geoscience and Remote Sensing（地质科学与遥感系）；Delft University of Technology（代尔夫特理工大学）；School of Engineering and Computer Science（工程与计算机科学学院）；Bern University of Applied Sciences（伯恩应用科学大学）
Korea Advanced Institute of Science and Technology（韩国科学技术院）
A `trajectory' refers to a trace generated by a moving object in geographical spaces, usually represented by of a series of chronologically ordered points, where each point consists of a geo-spatial coordinate set and a timestamp. Rapid advancements in location sensing and wireless communication technology enabled us to collect and store a massive amount of trajectory data. As a result, many researchers use trajectory data to analyze mobility of various moving objects. In this dissertation, we focus on the `urban vehicle trajectory,' which refers to trajectories of vehicles in urban traffic ne…
There has been increased interest in missing sensor data imputation, which is ubiquitous in the field of structural health monitoring (SHM) due to discontinuous sensing caused by sensor malfunction. To address this fundamental issue, this paper presents an incremental Bayesian tensor learning method for reconstruction of spatiotemporal missing data in SHM and forecasting of structural response. In particular, a spatiotemporal tensor is first constructed followed by Bayesian tensor factorization that extracts latent features for missing data imputation. To enable structural response forecasting…
MoE Enhanced Federated Learning for Spatiotemporal Prediction
Zhejiang University of Technology, Zhejiang Key Laboratory of Visual Information Intelligent Processing（浙江工业大学，浙江省可视信息智能处理重点实验室）；Jilin University（吉林大学）；City University of Hong Kong（香港城市大学）
Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario
SUSTech-UTokyo Joint Research Center on Super Smart City（南方科技大学-东京大学超级智慧城市联合研究中心）；Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Center for Spatial Information Science, University of Tokyo（东京大学空间信息科学中心）
Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework
University of Science and Technology of China (USTC)（中国科学技术大学）；Suzhou Institute for Advanced Research, USTC（中国科学技术大学苏州高等研究院）；State Key Laboratory of Resources and Environmental Information System（资源与环境信息系统国家重点实验室）
Spatiotemporal Heterogeneity of AI-Driven Traffic Flow Patterns and Land Use Interaction: A GeoAI-Based Analysis of Multimodal Urban Mobility
Department of Applied Mathematics（应用数学系）；Computer Science (DTU Compute), Technical University of Denmark, Kongens Lyngby, Denmark（计算机科学（DTU Compute），丹麦技术大学，Kongens Lyngby）
