flymeblack / ai4stf-papers

AI for Spatio-Temporal Forecasting Papers

This is a list of academic papers focusing on the frontier of AI for spatio-temporal forecasting. All the papers are collected from prestigious AI conferences and journals, covering a wide range of applications such as traffic forecasting, weather forecasting, and event prediction. Hope this repository can serve as an invaluable resource for researchers who are interested in this area.

Contents

• 0. Toolkits
• 1. Survey Papers
• 2. Research Papers by Year

0. Toolkits

1. UCTB: An Urban Computing Tool Box for Spatiotemporal Crowd Flow Prediction. Liyue Chen, Di Chai, Leye Wang. Preprint 2023. [website] [doc] [pdf]
2. Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction: A Unified Library and Performance Benchmark. Jiawei Jiang, Chengkai Han, Wenjun Jiang, Wayne Xin Zhao, Jingyuan Wang. Preprint 2023. [website] [doc] [pdf]
3. PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models. Benedek Rozemberczki, Paul Scherer, Yixuan He, George Panagopoulos, Alexander Riedel, Maria Astefanoaei, Oliver Kiss, Ferenc Beres, Guzmán López, Nicolas Collignon, Rik Sarkar. CIKM 2021. [website] [doc] [pdf]

1. Survey Papers

1. Graph Neural Network for Spatiotemporal Data: Methods and Applications. Yun Li, Dazhou Yu, Zhenke Liu, Minxing Zhang, Xiaoyun Gong, Liang Zhao. Preprint 2023. [pdf]
2. Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey. Guangyin Jin, Yuxuan Liang, Yuchen Fang, Jincai Huang, Junbo Zhang, Yu Zheng. Preprint 2023. [pdf]
3. Graph Neural Network for Traffic Forecasting: A Survey. Weiwei Jiang, Jiayun Luo. Expert Systems with Applications 2022. [pdf]
4. Urban Flows Prediction from Spatial-Temporal Data Using Machine Mearning: A Survey. Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang. Information Fusion 2020. [pdf]
5. A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges. David Alexander Tedjopurnomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, Kai Qin. TKDE 2020. [pdf]

2. Research Papers by Year

2023

1. Accurate Medium-Range Global Weather Forecasting with 3D Neural Networks. Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, Qi Tian. Nature 2023. [pdf]
2. Skilful Nowcasting of Extreme Precipitation with NowcastNet. Yuchen Zhang, Mingsheng Long, Kaiyuan Chen, Lanxiang Xing, Ronghua Jin, Michael I. Jordan, Jianmin Wang. Nature 2023. [pdf]
3. Interpretable Weather Forecasting for Worldwide Stations with a Unified Deep Model. Haixu Wu, Hang Zhou, Mingsheng Long, Jianmin Wang. Nature Machine Intelligence 2023. [pdf]
4. Sparse Graph Learning from Spatiotemporal Time Series. Andrea Cini, Daniele Zambon, Cesare Alippi. JMLR 2023. [pdf]
5. Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning. Zhengyang Zhou, Qihe Huang, Kuo Yang, Kun Wang, Xu Wang, Yudong Zhang, Yuxuan Liang, Yang Wang. KDD 2023. [pdf]
6. Localised Adaptive Spatial-Temporal Graph Neural Network. Wenying Duan, Xiaoxi He, Zimu Zhou, Lothar Thiele, Hong Rao. KDD 2023. [pdf]
7. Spatio-temporal Diffusion Point Processes. Yuan Yuan, Jingtao Ding, Chenyang Shao, Depeng Jin, Yong Li. KDD 2023. [pdf]
8. Transferable Graph Structure Learning for Graph-Based Traffic Forecasting Across Cities. Yilun Jin, Kai Chen, Qiang Yang. KDD 2023. [pdf]
9. Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training. Fan Liu, Weijia Zhang, Hao Liu. KDD 2023. [pdf]
10. Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction. Binwu Wang, Yudong Zhang, Xu Wang, Pengkun Wang, Zhengyang Zhou, Lei Bai, Yang Wang. KDD 2023. [pdf]
11. Spatial Heterophily Aware Graph Neural Networks. Congxi Xiao, Jingbo Zhou, Jizhou Huang, Tong Xu, Hui Xiong. KDD 2023. [pdf]
12. FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks. Mridul Gupta, Hariprasad Kodamana, Sayan Ranu. KDD 2023. [pdf]
13. Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer. Yang Zhang, Lingbo Liu, Xinyu Xiong, Guanbin Li, Guoli Wang, Liang Lin. IJCAI 2023. [pdf]
14. Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data. Shengchao Chen, Guodong Long, Tao Shen, Jing Jiang. IJCAI 2023. [pdf]
15. Optimization-driven Demand Prediction Framework for Suburban Dynamic Demand-Responsive Transport Systems. Louis Zigrand, Roberto Wolfler Calvo, Emiliano Traversi, Pegah Alizadeh. IJCAI 2023. [pdf]
16. Not Only Pairwise Relationships: Fine-Grained Relational Modeling for Multivariate Time Series Forecasting. Jinming Wu, Qi Qi, Jingyu Wang, Haifeng Sun, Zhikang Wu, Zirui Zhuang, Jianxin Liao. IJCAI 2023. [pdf]
17. Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting. Yunhao Zhang, Junchi Yan. ICLR 2023. [pdf]
18. Automated Spatio-Temporal Graph Contrastive Learning. Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li, Siu-Ming Yiu. WWW 2023. [pdf]
19. AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting. Xinle Wu, Dalin Zhang, Miao Zhang, Chenjuan Guo, Bin Yang, Christian S. Jensen. SIGMOD 2023. [pdf]
20. LightCTS: A Lightweight Framework for Correlated Time Series Forecasting. Zhichen Lai, Dalin Zhang, Huan Li, Christian S. Jensen, Hua Lu, Yan Zhao. SIGMOD 2023. [pdf]
21. ST4ML: Machine Learning Oriented Spatio-Temporal Data Processing at Scale. Kaiqi Liu, Panrong Tong, Mo Li, Yue Wu, Jianqiang Huang. SIGMOD 2023. [pdf]
22. Spatio-Temporal Neural Structural Causal Models for Bike Flow Prediction. Pan Deng, Yu Zhao, Junting Liu, Xiaofeng Jia, Mulan Wang. AAAI 2023. [pdf]
23. Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction. Jiahao Ji, Jingyuan Wang, Chao Huang, Junjie Wu, Boren Xu, Zhenhe Wu, Junbo Zhang, Yu Zheng. AAAI 2023. [pdf]
24. Easy Begun Is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout. Hongjun Wang, Jiyuan Chen, Tong Pan, Zipei Fan, Xuan Song, Renhe Jiang, Lingyu Zhang, Yi Xie, Zhongyi Wang, Boyuan Zhang. AAAI 2023. [pdf]
25. AirFormer: Predicting Nationwide Air Quality in China with Transformers. Yuxuan Liang, Yutong Xia, Songyu Ke, Yiwei Wang, Qingsong Wen, Junbo Zhang, Yu Zheng, Roger Zimmermann. AAAI 2023. [pdf]
26. AutoSTL: Automated Spatio-Temporal Multi-Task Learning. Zijian Zhang, Xiangyu Zhao, Hao Miao, Chunxu Zhang, Hongwei Zhao, Junbo Zhang. AAAI 2023. [pdf]
27. Scalable Spatiotemporal Graph Neural Networks. Andrea Cini, Ivan Marisca, Filippo Maria Bianchi, Cesare Alippi. AAAI 2023. [pdf]
28. Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling. Yuchen Fang, Kan Ren, Caihua Shan, Yifei Shen, You Li, Weinan Zhang, Yong Yu, Dongsheng Li. AAAI 2023. [pdf]
29. Spatio-Temporal Meta-Graph Learning for Traffic Forecasting. Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, Toyotaro Suzumura. AAAI 2023. [pdf]
30. Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction. Guangyin Jin, Lingbo Liu, Fuxian Li, Jincai Huang. AAAI 2023. [pdf]
31. SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting. Lei Chen, Fei Du, Yuan Hu, Zhibin Wang, Fan Wang. AAAI 2023. [pdf]

2022

1. Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting. Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, Christian S. Jensen. VLDB 2022. [pdf]
2. AutoST: Towards the Universal Modeling of Spatio-temporal Sequences. Jianxin Li, Shuai Zhang, Hui Xiong, Haoyi Zhou. NeurIPS 2022. [pdf]
3. Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks. Yijing Liu, Qinxian Liu, Jian-Wei Zhang, Haozhe Feng, Zhongwei Wang, Zihan Zhou, Wei Chen. NeurIPS 2022. [pdf]
4. Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models. Fan Liu, Hao Liu, Wenzhao Jiang. NeurIPS 2022. [pdf]
5. Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. Zhihan Gao, Xingjian Shi, Hao Wang, Yi Zhu, Yuyang Wang, Mu Li, Dit-Yan Yeung. NeurIPS 2022. [pdf]
6. Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting. Zezhi Shao, Zhao Zhang, Fei Wang, Yongjun Xu. KDD 2022. [pdf]
7. Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting. Junchen Ye, Zihan Liu, Bowen Du, Leilei Sun, Weimiao Li, Yanjie Fu, Hui Xiong. KDD 2022. [pdf]
8. MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting. Dachuan Liu, Jin Wang, Shuo Shang, Peng Han. KDD 2022. [pdf]
9. Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning. Rongfan Li, Ting Zhong, Xinke Jiang, Goce Trajcevski, Jin Wu, Fan Zhou. KDD 2022. [pdf]
10. Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer. Bin Lu, Xiaoying Gan, Weinan Zhang, Huaxiu Yao, Luoyi Fu, Xinbing Wang. KDD 2022. [pdf]
11. Selective Cross-City Transfer Learning for Traffic Prediction via Source City Region Re-Weighting. Yilun Jin, Kai Chen, Qiang Yang. KDD 2022. [pdf]
12. Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction. Liangzhe Han, Xiaojian Ma, Leilei Sun, Bowen Du, Yanjie Fu, Weifeng Lv, Hui Xiong. KDD 2022. [pdf]
13. Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator. Tailin Wu, Qinchen Wang, Yinan Zhang, Rex Ying, Kaidi Cao, Rok Sosič, Ridwan Jalali, Hassan Hamam, Marko Maucec, Jure Leskovec. KDD 2022. [pdf]
14. Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention. Wei Shao, Zhiling Jin, Shuo Wang, Yufan Kang, Xiao Xiao, Hamid Menouar, Zhaofeng Zhang, Junshan Zhang, Flora D. Salim. IJCAI 2022. [pdf]
15. FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting. Xuan Rao, Hao Wang, Liang Zhang, Jing Li, Shuo Shang, Peng Han. IJCAI 2022. [pdf]
16. DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting. Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, Pyang Li. ICML 2022. [pdf]
17. Pyramid: Enabling Hierarchical Neural Networks with Edge Computing. Qiang He, Zeqian Dong, Feifei Chen, Shuiguang Deng, Weifa Liang, Yun Yang. WWW 2022. [pdf]
18. TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting. Yuzhou Chen, Ignacio Segovia-Dominguez, Baris Coskunuzer, Yulia R. Gel. ICLR 2022. [pdf]
19. Graph-Guided Network for Irregularly Sampled Multivariate Time Series. Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, Marinka Zitnik. ICLR 2022. [pdf]
20. Graph Neural Controlled Differential Equations for Traffic Forecasting. Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, Noseong Park. AAAI 2022. [pdf]

2021

1. Skillful Precipitation Nowcasting using Deep Generative Models of Radar. Suman Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, Sam Madge, Rachel Prudden, Amol Mandhane, Aidan Clark, Andrew Brock, Karen Simonyan, Raia Hadsell, Niall Robinson, Ellen Clancy, Alberto Arribas, Shakir Mohamed. Nature 2021. [pdf]
2. ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting. Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, Ivor W. Tsang. KDD 2021. [pdf]
3. Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting. Zheng Fang, Qingqing Long, Guojie Song, Kunqing Xie. KDD 2021. [pdf]
4. Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting. Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, Hui Xiong. KDD 2021. [pdf]
5. Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning. Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Liefeng Bo, Xiyue Zhang, Tianyi Chen. IJCAI 2021. [pdf]
6. TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning. Xu Chen, Junshan Wang, Kunqing Xie. IJCAI 2021. [pdf]
7. AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph. Zheyi Pan, Songyu Ke, Xiaodu Yang, Yuxuan Liang, Yong Yu, Junbo Zhang, Yu Zheng. WWW 2021. [pdf]
8. REST: Reciprocal Framework for Spatiotemporal-coupled Predictions. Haozhe Lin, Yushun Fan, Jia Zhang, Bing Bai. WWW 2021. [pdf]
9. Discrete Graph Structure Learning for Forecasting Multiple Time Series. Chao Shang, Jie Chen, Jinbo Bi. ICLR 2021. [pdf]
10. Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting. Mengzhang Li, Zhanxing Zhu. AAAI 2021. [pdf]
11. FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting. Boris N. Oreshkin, Arezou Amini, Lucy Coyle, Mark Coates. AAAI 2021. [pdf]
12. Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks. Jindong Han, Hao Liu, Hengshu Zhu, Hui Xiong, Dejing Dou. AAAI 2021. [pdf]
13. 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. AAAI 2021. [pdf]
14. Spatiotemporal Graph Neural Network for Performance Prediction of Photovoltaic Power Systems. Ahmad Maroof Karimi, Yinghui Wu, Mehmet Koyutürk, Roger H. French. AAAI 2021. [pdf]
15. Hierarchical Graph Convolution Network for Traffic Forecasting. Kan Guo, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, Baocai Yin. AAAI 2021. [pdf]

2020

1. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. Lei Bai, Lina Yao, Can Li, Xianzhi Wang, Can Wang. NeurIPS 2020. [pdf]
2. Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang. NeurIPS 2020. [pdf]
3. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang. KDD 2020. [pdf]
4. AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction. Ting Li, Junbo Zhang, Kainan Bao, Yuxuan Liang, Yexin Li, Yu Zheng. KDD 2020. [pdf]
5. Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data. Rui Dai, Shenkun Xu, Qian Gu, Chenguang Ji, Kaikui Liu. KDD 2020. [pdf]
6. LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks. Rongzhou Huang, Chuyin Huang, Yubao Liu, Genan Dai, Weiyang Kong. IJCAI 2020. [pdf]
7. Traffic Flow Prediction via Spatial Temporal Graph Neural Network. Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, Jian Yu. WWW 2020. [pdf]
8. Spatio-Temporal Graph Structure Learning for Traffic Forecasting. Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. AAAI 2020. [pdf]
9. GMAN: A Graph Multi-Attention Network for Traffic Prediction. Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi. AAAI 2020. [pdf]
10. Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting. Weiqi Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, Xiaojie Feng. AAAI 2020. [pdf]
11. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. Chao Song, Youfang Lin, Shengnan Guo, Huaiyu Wan. AAAI 2020. [pdf]

2019

1. Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning. Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang. KDD 2019. [pdf]
2. Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network. Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Xinran Tong, Hui Xiong. KDD 2019. [pdf]
3. GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction. Shen Fang, Qi Zhang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. IJCAI 2019. [pdf]
4. STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting. Lei Bai, Lina Yao, Salil S. Kanhere, Xianzhi Wang, Quan Z. Sheng. IJCAI 2019. [pdf]
5. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang. IJCAI 2019. [pdf]
6. Gated Residual Recurrent Graph Neural Networks for Traffic Prediction. Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, Zeng Zeng. AAAI 2019. [pdf]
7. Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting. Zulong Diao, Xin Wang, Dafang Zhang, Yingru Liu, Kun Xie, Shaoyao He. AAAI 2019. [pdf]
8. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan. AAAI 2019. [pdf]
9. Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction. Youru Li, Zhenfeng Zhu, Deqiang Kong, Meixiang Xu, Yao Zhao. AAAI 2019. [pdf]
10. DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis. Ziqian Lin, Jie Feng, Ziyang Lu, Yong Li, Depeng Jin. AAAI 2019. [pdf]
11. Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting. Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, Yan Liu. AAAI 2019. [pdf]

2018

1. Deep Distributed Fusion Network for Air Quality Prediction. Xiuwen Yi, Junbo Zhang, Zhaoyuan Wang, Tianrui Li, Yu Zheng. KDD 2018. [pdf]
2. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu. ICLR 2018. [pdf]
3. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Bing Yu, Haoteng Yin, Zhanxing Zhu. IJCAI 2018. [pdf]
4. GeoMAN: multi-level attention networks for geo-sensory time series prediction. Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, Yu Zheng. IJCAI 2018. [pdf]

2017

1. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. Junbo Zhang, Yu Zheng, Dekang Qi. AAAI 2019. [pdf]