Deep Learning for Spatio-Temporal Data

Last updated on Aug 18, 2024

Advances in location-acquisition and wireless communication technologies have led to wider availability of spatio-temporal (ST) data. Deep neural networks (DNNs) have been successfully applied to various problems, such as computer vision, speech recognition, natural language understanding. Being different from these domains, ST data has unique spatial properties (i.e. geographical hierarchy and distance) and temporal properties (i.e. closeness, period and trend). It is very challenging to capture all of these ST properties simultaneously.

Tutorial on Deep Learning for Spatio-Temporal Data in Urban Computing Summer School 2020

Deep Learning STAI

Publications

Spatio-Temporal Consistency Enhanced Differential Network for Interpretable Indoor Temperature Prediction

Dekang Qi, Xiuwen Yi*, Chengjie Guo, Yanyong Huang, Junbo Zhang, Tianrui Li, Yu Zheng

DiffUFlow: Robust Fine-grained Urban Flow Inference with Denoising Diffusion Model

Yuhao Zheng, Lian Zhong, Senzhang Wang, Yu Yang, Weixi Gu, Junbo Zhang, Jianxin Wang

MLPST: MLP is All You Need for Spatio-Temporal Prediction

Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S. Joe Qin, Hongwei Zhao

AirFormer: Predicting Nationwide Air Quality in China with Transformers

Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic growth. Forecasting …

Yuxuan Liang, Yutong Xia, Songyu Ke, Yiwei Wang, Qingsong Wen, Junbo Zhang, Yu Zheng, Roger Zimmermann

AirFormer: Predicting Nationwide Air Quality in China with Transformers

Mixed-Order Relation-Aware Recurrent Neural Networks for Spatio-Temporal Forecasting

Yuxuan Liang, Kun Ouyang, Yiwei Wang, Zheyi Pan*, Yifang Yin, Hongyang Chen, Junbo Zhang, Yu Zheng, David S. Rosenblum, Roger Zimmermann

Mixed-Order Relation-Aware Recurrent Neural Networks for Spatio-Temporal Forecasting

TrajFormer: Efficient Trajectory Classification with Transformers

Transformers have been an efficient alternative to recurrent neural networks in many sequential learning tasks. When adapting …

Yuxuan Liang, Kun Ouyang, Yiwei Wang, Xu Liu, Hongyang Chen, Junbo Zhang, Yu Zheng, Roger Zimmermann

TrajFormer: Efficient Trajectory Classification with Transformers

Precision CityShield Against Hazardous Chemicals Threats via Location Mining and Self-Supervised Learning

Jiahao Ji*, Jingyuan Wang, Junjie Wu, Boyang Han, Junbo Zhang, Yu Zheng

Precision CityShield Against Hazardous Chemicals Threats via Location Mining and Self-Supervised Learning

Forecasting Fine-grained Urban Flow via Spatio-temporal Contrastive Self-Supervision

Hao Qu, Yongshun Gong, Meng Chen, Junbo Zhang, Yu Zheng, Yilong Yin

Forecasting Fine-grained Urban Flow via Spatio-temporal Contrastive Self-Supervision

Multi-Memory enhanced Separation Network for Indoor Temperature Prediction

Zhewen Duan, Xiuwen Yi*, Peng Li, Dekang Qi, Yexin Li, Haoran Xu, Yanyong Huang, Junbo Zhang, Yu Zheng

Multi-Memory enhanced Separation Network for Indoor Temperature Prediction

Fine-grained Urban Flow Inference with Incomplete Data

Jiyue Li*, Senzhang Wang, Jiaqiang Zhang, Hao Miao, Junbo Zhang, Philip Yu

Fine-grained Urban Flow Prediction

Yuxuan Liang, Kun Ouyang, Junkai Sun*, Yiwei Wang, Junbo Zhang, Yu Zheng, David S.Rosenblum, Roger Zimmermann

Fine-grained Urban Flow Prediction

Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network

Xiyue Zhang, Chao Huang*, Yong Xu, Lianghao Xia, Peng Dai, Liefeng Bo, Junbo Zhang, Yu Zheng

Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network

Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

IEEE TKDE, 2020

Junkai Sun*, Junbo Zhang#, Qiaofei Li, Xiuwen Yi*, Yuxuan Liang, Yu Zheng

Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics

Yuxuan Liang, Kun Ouyang, Yiwei Wang, Ye Liu, Junbo Zhang, Yu Zheng, David S. Rosenblum

Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics

Spatio-Temporal Meta Learning for Urban Traffic Prediction

Zheyi Pan*, Wentao Zhang, Yuxuan Liang, Weinan Zhang, Yong Yu, Junbo Zhang#, Yu Zheng

Spatio-Temporal Meta Learning for Urban Traffic Prediction

Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning

Predicting flows (e.g. the traffic of vehicles, crowds and bikes), consisting of the in-out traffic at a node and transitions between …

Junbo Zhang#, Yu Zheng, Junkai Sun*, Dekang Qi

Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning

Urban flow prediction from spatiotemporal data using machine learning: A survey

Information Fusion

Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang

Urban flow prediction from spatiotemporal data using machine learning: A survey

CityGuard: Citywide Fire Risk Forecasting Using A Machine Learning Approach

Qianru Wang*, Junbo Zhang#, Bin Guo, Zexia Hao, Yifang Zhou, Junkai Sun*, Zhiwen Yu, Yu Zheng

CityGuard: Citywide Fire Risk Forecasting Using A Machine Learning Approach

Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction

CIKM 2019, Applied Research Track (acceptance rate: 21.8%)

Zheyi Pan*, Zhaoyuan Wang, Weifeng Wang, Yong Yu, Junbo Zhang#, Yu Zheng

Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction

CityTraffic: Modeling Citywide Traffic via Neural Memorization and Generalization Approach

CIKM 2019, Applied Research Track (acceptance rate: 21.8%)

Xiuwen Yi*, Zhewen Duan, Ting Li, Junbo Zhang#, Tianrui Li, Yu Zheng

CityTraffic: Modeling Citywide Traffic via Neural Memorization and Generalization Approach

Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning

[ORAL] KDD 2019, Research Track (acceptance rate: 14.2%)

Zheyi Pan*, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang

Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning

UrbanFM: Inferring Fine-Grained Urban Flows

KDD 2019, ADS Track (acceptance rate: 20.7%)

Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S. Rosenblum, Yu Zheng

UrbanFM: Inferring Fine-Grained Urban Flows

DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction

As urban crimes (e.g., burglary and robbery) negatively impact our everyday life and must be addressed in a timely manner, predict- ing …

Chao Huang*, Junbo Zhang, Yu Zheng, Nitesh V. Chawla

Deep Distributed Fusion Network for Air Quality Prediction

KDD 2018, ADS Track (acceptance rate: 14.0%)

Xiuwen Yi*, Junbo Zhang#, Zhaoyuan Wang, Tianrui Li, Yu Zheng

Deep Distributed Fusion Network for Air Quality Prediction

GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction

Numerous sensors have been deployed in different geospatial locations to continuously and cooperatively monitor the surrounding …

Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi*, Yu Zheng

GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction

Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks

Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi*, Tianrui Li

Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks

When Will You Arrive Estimating Travel Time Based on Deep Neural Networks

Estimating the travel time of any path (denoted by a sequence of connected road segments) in a city is of great importance to traffic …

Dong Wang*, Junbo Zhang, Wei Cao, Jian Li, Yu Zheng

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

We proposed a deep-learning-based approach, called ST-ResNet, to collectively forecast the inflow and outflow of crowds in each and every region of a city.

Junbo Zhang, Yu Zheng, Dekang Qi

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

DNN-Based Prediction Model for Spatio-Temporal Data

Advances in location-acquisition and wireless communication technologies have led to wider availability of spatio-temporal (ST) data, …

Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi*