Fine-Grained Urban Flow Prediction
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David S. Rosenblum | Roger Zimmermann | Yu Zheng | Junbo Zhang | Yuxuan Liang | Junkai Sun | Yiwei Wang | Kun Ouyang | Roger Zimmermann | Yiwei Wang | Junkai Sun | Junbo Zhang | Yu Zheng | Yuxuan Liang | Kun Ouyang
[1] Shuicheng Yan,et al. Graph-Based Global Reasoning Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[2] David S. Rosenblum,et al. UrbanFM: Inferring Fine-Grained Urban Flows , 2019, KDD.
[3] Jing Jiang,et al. Graph WaveNet for Deep Spatial-Temporal Graph Modeling , 2019, IJCAI.
[4] Jieping Ye,et al. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction , 2018, AAAI.
[5] Diansheng Guo,et al. A Sequential Convolution Network for Population Flow Prediction with Explicitly Correlation Modelling , 2020, IJCAI.
[6] Xianfeng Tang,et al. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction , 2018, AAAI.
[7] Xing Xie,et al. Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.
[8] Liang Lin,et al. Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction , 2021, IEEE Transactions on Intelligent Transportation Systems.
[9] Philippe Muller,et al. Topological Spatio–Temporal Reasoning and Representation , 2002, Comput. Intell..
[10] Yong Li,et al. DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis , 2019, AAAI.
[11] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[12] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[13] Cyrus Shahabi,et al. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting , 2017, ICLR.
[14] Jitendra Malik,et al. Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[15] Yu Zheng,et al. Fine-Grained Urban Flow Inference , 2020, IEEE Transactions on Knowledge and Data Engineering.
[16] Junbo Zhang,et al. Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning , 2020, IEEE Transactions on Knowledge and Data Engineering.
[17] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[18] Bolei Zhou,et al. Temporal Relational Reasoning in Videos , 2017, ECCV.
[19] Abhinav Gupta,et al. Videos as Space-Time Region Graphs , 2018, ECCV.
[20] Qi Zhang,et al. GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction , 2019, IJCAI.
[21] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Shijie Li,et al. Deep Spatial–Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting , 2019, IEEE Transactions on Intelligent Transportation Systems.
[23] Nitesh V. Chawla,et al. Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting , 2020, WWW.
[24] Junbo Zhang,et al. Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning , 2019, KDD.
[25] My T. Thai,et al. Big Data in Complex and Social Networks , 2016 .
[26] Nitesh V. Chawla,et al. MiST: A Multiview and Multimodal Spatial-Temporal Learning Framework for Citywide Abnormal Event Forecasting , 2019, WWW.
[27] Yu Zheng,et al. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.
[28] Ambuj K. Singh,et al. FCCF: forecasting citywide crowd flows based on big data , 2016, SIGSPATIAL/GIS.
[29] Razvan Pascanu,et al. A simple neural network module for relational reasoning , 2017, NIPS.
[30] Xiuwen Yi,et al. DNN-based prediction model for spatio-temporal data , 2016, SIGSPATIAL/GIS.
[31] Xuan Song,et al. Prediction of human emergency behavior and their mobility following large-scale disaster , 2014, KDD.
[32] Ning Feng,et al. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting , 2019, AAAI.
[33] Abhinav Gupta,et al. Beyond Grids: Learning Graph Representations for Visual Recognition , 2018, NeurIPS.
[34] D. Dickey,et al. Testing for unit roots in autoregressive-moving average models of unknown order , 1984 .
[35] Yu Zheng,et al. Inferring Traffic Cascading Patterns , 2017, SIGSPATIAL/GIS.
[36] Gao Cong,et al. Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns , 2018, IJCAI.
[37] Liang Lin,et al. Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction , 2019, IEEE Transactions on Intelligent Transportation Systems.
[38] Zhanxing Zhu,et al. Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting , 2017, IJCAI.
[39] C. Sims,et al. Vector Autoregressions , 1999 .
[40] Zhongfei Zhang,et al. Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-Grained Air Quality , 2017, IEEE Transactions on Knowledge and Data Engineering.
[41] Cesare Alippi,et al. Mincut pooling in Graph Neural Networks , 2019, ArXiv.
[42] Licia Capra,et al. Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.
[43] Yong Yu,et al. Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction , 2019, CIKM.
[44] Xuan Song,et al. CityMomentum: an online approach for crowd behavior prediction at a citywide level , 2015, UbiComp.
[45] Yu Zheng,et al. Traffic prediction in a bike-sharing system , 2015, SIGSPATIAL/GIS.
[46] Nitesh V. Chawla,et al. DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction , 2018, CIKM.
[47] Enhua Wu,et al. Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.