206 citations · 825 across the 18 of their papers we have counts for
19 papers
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook
Xingchen Zou, Yibo Yan, Xixuan Hao +8
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources…
Graph Neural Processes for Spatio-Temporal Extrapolation
Junfeng Hu, Yuxuan Liang, Zhencheng Fan +3
We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph. This task is crucial as sensors that collect data a…
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
Guangyin Jin, Yuxuan Liang, Yuchen Fang +4
With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-tempora…
Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction
Jiahao Ji, Jingyuan Wang, Chao Huang +5
Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to mode…
AirFormer: Predicting Nationwide Air Quality in China with Transformers
Yuxuan Liang, Yutong Xia, Songyu Ke +5
Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic and social growth. Forecasting air quality has become an increas…
A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling
Gaode Chen, Yijun Su, Xinghua Zhang +6
Data insufficiency problems (i.e., data missing and label scarcity) caused by inadequate services and infrastructures or imbalanced development levels of cities have seriously affe…
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Xiyue Zhang, Chao Huang, Yong Xu +5
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public ri…
When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?
Xu Liu, Yuxuan Liang, Chao Huang +3
Deep learning models are modern tools for spatio-temporal graph (STG) forecasting. Though successful, we argue that data scarcity is a key factor limiting their recent improvements…
ReAD: A Regional Anomaly Detection Framework Based on Dynamic Partition
Huaishao Luo, Chuishi Meng, Bowen Wu +3
The detection of the abnormal area from urban data is a significant research problem. However, to the best of our knowledge, previous methods designed on spatio-temporal anomalies…
Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics
Yuxuan Liang, Kun Ouyang, Yiwei Wang +4
Citywide crowd flow analytics is of great importance to smart city efforts. It aims to model the crowd flow (e.g., inflow and outflow) of each region in a city based on historical…
Federated Extra-Trees with Privacy Preserving
Yang Liu, Mingxin Chen, Wenxi Zhang +2
It is commonly observed that the data are scattered everywhere and difficult to be centralized. The data privacy and security also become a sensitive topic. The laws and regulation…
Fine-Grained Urban Flow Inference
Kun Ouyang, Yuxuan Liang, Ye Liu +4
The ubiquitous deployment of monitoring devices in urban flow monitoring systems induces a significant cost for maintenance and operation. A technique is required to reduce the num…
Federated Forest
Yang Liu, Yingting Liu, Zhijie Liu +3
Most real-world data are scattered across different companies or government organizations, and cannot be easily integrated under data privacy and related regulations such as the Eu…
Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks
Junkai Sun, Junbo Zhang, Qiaofei Li +3
Being able to predict the crowd flows in each and every part of a city, especially in irregular regions, is strategically important for traffic control, risk assessment, and public…
UrbanFM: Inferring Fine-Grained Urban Flows
Yuxuan Liang, Kun Ouyang, Lin Jing +5
Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-l…
HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting
Zheyi Pan, Yuxuan Liang, Junbo Zhang +3
Spatio-temporal (ST) data, which represent multiple time series data corresponding to different spatial locations, are ubiquitous in real-world dynamic systems, such as air quality…
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks
Junbo Zhang, Yu Zheng, Dekang Qi +3
Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dep…
Predicting Urban Water Quality with Ubiquitous Data
Ye Liu, Yuxuan Liang, Shuming Liu +2
Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. However, predicting the urba…
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
Junbo Zhang, Yu Zheng, Dekang Qi
Forecasting the flow of crowds is of great importance to traffic management and public safety, yet a very challenging task affected by many complex factors, such as inter-region tr…