Urban Regional Function Guided Traffic Flow Prediction
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Guanbin Li | Lingbo Liu | Fan Zhou | Liang Lin | Kuo Wang | Yang Liu
[1] Guanfeng Liu,et al. Spatial-temporal dependence and similarity aware traffic flow forecasting , 2023, Inf. Sci..
[2] Guanbin Li,et al. Hybrid-Order Representation Learning for Electricity Theft Detection , 2023, IEEE Transactions on Industrial Informatics.
[3] Yongjian Yang,et al. Dynamic traffic correlations based spatio-temporal graph convolutional network for urban traffic prediction , 2022, Inf. Sci..
[4] Libing Wu,et al. A spatio-temporal sequence-to-sequence network for traffic flow prediction , 2022, Inf. Sci..
[5] Guanbin Li,et al. Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering , 2022, ArXiv.
[6] Faculty of Information Technology,et al. A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism , 2022, Inf. Sci..
[7] Guanbin Li,et al. Causal Reasoning Meets Visual Representation Learning: A Prospective Study , 2022, Machine Intelligence Research.
[8] Liang Lin,et al. Temporal Contrastive Graph for Self-supervised Video Representation Learning , 2021, ArXiv.
[9] Yu Zheng,et al. Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks , 2019, IEEE Transactions on Knowledge and Data Engineering.
[10] Bowen Du,et al. Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning , 2021, Inf. Sci..
[11] David S. Rosenblum,et al. Fine-Grained Urban Flow Prediction , 2021, WWW.
[12] Ye Yuan,et al. Modeling Citywide Crowd Flows using Attentive Convolutional LSTM , 2021, 2021 IEEE 37th International Conference on Data Engineering (ICDE).
[13] Yang Liu,et al. Semantics-Aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition , 2020, IEEE Transactions on Image Processing.
[14] Muhammad Zakarya,et al. Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks , 2021, Inf. Sci..
[15] Jingyuan Wang,et al. Interpretable Spatiotemporal Deep Learning Model for Traffic Flow Prediction based on Potential Energy Fields , 2020, 2020 IEEE International Conference on Data Mining (ICDM).
[16] Kaigui Bian,et al. TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting , 2020, 2020 IEEE International Conference on Data Mining (ICDM).
[17] Xinbing Wang,et al. STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction , 2020, CIKM.
[18] Chenglu Wen,et al. DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction , 2020, IEEE Transactions on Intelligent Transportation Systems.
[19] Hongzhi Shi,et al. Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network , 2020, 2020 IEEE 36th International Conference on Data Engineering (ICDE).
[20] Tao Yang,et al. Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition , 2019, IEEE Transactions on Image Processing.
[21] Cheng Wang,et al. GMAN: A Graph Multi-Attention Network for Traffic Prediction , 2019, AAAI.
[22] Xiangnan Kong,et al. TrafficGAN: Off-Deployment Traffic Estimation with Traffic Generative Adversarial Networks , 2019, 2019 IEEE International Conference on Data Mining (ICDM).
[23] Junbo Zhang,et al. Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning , 2019, KDD.
[24] Ning Feng,et al. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting , 2019, AAAI.
[25] Jieping Ye,et al. Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting , 2019, AAAI.
[26] Jing Li,et al. Hierarchically Learned View-Invariant Representations for Cross-View Action Recognition , 2018, IEEE Transactions on Circuits and Systems for Video Technology.
[27] Pengpeng Zhao,et al. LC-RNN: A Deep Learning Model for Traffic Speed Prediction , 2018, IJCAI.
[28] Jing Li,et al. Global Temporal Representation Based CNNs for Infrared Action Recognition , 2018, IEEE Signal Processing Letters.
[29] Jieping Ye,et al. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction , 2018, AAAI.
[30] Zhaoyang Lu,et al. Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition , 2018, Complex..
[31] Ruoyu Li,et al. Adaptive Graph Convolutional Neural Networks , 2018, AAAI.
[32] Zhanxing Zhu,et al. Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting , 2017, IJCAI.
[33] Cyrus Shahabi,et al. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting , 2017, ICLR.
[34] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[35] Nikos Komodakis,et al. Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Pierre Vandergheynst,et al. Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..
[37] Yu Zheng,et al. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.
[38] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[39] Ambuj K. Singh,et al. FCCF: forecasting citywide crowd flows based on big data , 2016, SIGSPATIAL/GIS.
[40] Xiuwen Yi,et al. DNN-based prediction model for spatio-temporal data , 2016, SIGSPATIAL/GIS.
[41] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[42] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Donald F. Towsley,et al. Diffusion-Convolutional Neural Networks , 2015, NIPS.
[44] Xuan Song,et al. CityMomentum: an online approach for crowd behavior prediction at a citywide level , 2015, UbiComp.
[45] Petros A. Ioannou,et al. Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data , 2015, IEEE Transactions on Intelligent Transportation Systems.
[46] Zhen Qian,et al. Road Traffic Congestion Monitoring in Social Media with Hinge-Loss Markov Random Fields , 2014, 2014 IEEE International Conference on Data Mining.
[47] Reinhard Klette,et al. Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction , 2014, IEEE Transactions on Intelligent Transportation Systems.
[48] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[49] Xuan Song,et al. Prediction of human emergency behavior and their mobility following large-scale disaster , 2014, KDD.
[50] Joan Bruna,et al. Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.
[51] Fei-Yue Wang,et al. Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.
[52] D. T. Lee,et al. Travel-time prediction with support vector regression , 2004, IEEE Transactions on Intelligent Transportation Systems.
[53] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[54] Spyros Makridakis,et al. ARMA Models and the Box–Jenkins Methodology , 1997 .