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We all depend on mobility, and vehicular transportation affects the daily lives of most of us.
Graph WaveNet for Deep Spatial-Temporal Graph Modeling. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019 . 1907–1913
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019a · 1913
Earlier work this paper cites.
A smoothness priors–state space modeling of time series with trend and seasonality
Genshiro Kitagawa and Will Gersch. 1984 · 1984
Earlier work this paper cites.
Freeway performance measurement system: mining loop detector data
Chao Chen, Karl Petty, Alexander Skabardonis, Pravin Varaiya, and Zhanfeng Jia. 2001 · 2001
Earlier work this paper cites.
Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results
Billy M Williams and Lester A Hoel. 2003 · 2003
Earlier work this paper cites.
New introduction to multiple time series analysis
Helmut Lütkepohl. 2005 · 2005
Earlier work this paper cites.
Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 315–323
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011 · 2011
Earlier work this paper cites.
Transportation systems engineering: theory and methods . Vol. 49
Ennio Cascetta. 2013 · 2013
Earlier work this paper cites.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst. 2013 · 2013
Earlier work this paper cites.
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014 · 2014
Earlier work this paper cites.
On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. In SSST@EMNLP . 103–111
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Big data and its technical challenges
Hosagrahar V Jagadish, Johannes Gehrke, Alexandros Labrinidis, Yannis Papakonstantinou, Jignesh M Patel, Raghu Ramakrishnan, and Cyrus Shahabi. 2014 · 2014
Earlier work this paper cites.
Traffic flow prediction with big data: a deep learning approach
Yisheng Lv, Yanjie Duan, Wenwen Kang, Zhengxi Li, and Fei-Yue Wang. 2014 · 2014
Earlier work this paper cites.
Urban computing: concepts, methodologies, and applications
Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014 · 2014
Earlier work this paper cites.
Short-term traffic flow prediction using seasonal ARIMA model with limited input data
S Vasantha Kumar and Lelitha Vanajakshi. 2015 · 2015
Earlier work this paper cites.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain . 3837–3845
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Earlier work this paper cites.
Integrating granger causality and vector auto-regression for traffic prediction of large-scale WLANs
Zheng Lu, Chen Zhou, Jing Wu, Hao Jiang, and Songyue Cui. 2016 · 2016
Earlier work this paper cites.
Multi-Scale Context Aggregation by Dilated Convolutions. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings
Fisher Yu and Vladlen Koltun. 2016 · 2016
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings
Thomas N. Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018 · 2018
Cited alongside, same era.
Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence . 914–921
Chao Song, Youfang Lin, Shengnan Guo, and Huaiyu Wan. 2020 · 2020
Later among the works it cites.
DeepTRANS: a deep learning system for public bus travel time estimation using traffic forecasting
Luan Tran, Min Y Mun, Matthew Lim, Jonah Yamato, Nathan Huh, and Cyrus Shahabi. 2020 · 2020
Later among the works it cites.
Traffic flow prediction via spatial temporal graph neural network. In Proceedings of The Web Conference 2020 . 1082–1092
Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu. 2020 · 2020
Later among the works it cites.
Connecting the dots: Multivariate time series forecasting with graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 753–763
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. 2020 · 2020
Later among the works it cites.
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Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 . 2588–2595
Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018 · 2018
Cited alongside, same era.
Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence . 3634–3640
Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2018 · 2018
Cited alongside, same era.
Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 984–992
Zhuoning Yuan, Xun Zhou, and Tianbao Yang. 2018 · 2018
Cited alongside, same era.
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. In Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018 . 339–349
Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung. 2018 · 2018
Cited alongside, same era.
Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In international conference on machine learning . PMLR, 21–29
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019 · 2019
Cited alongside, same era.
Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence . 922–929
Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019 · 2019
Cited alongside, same era.
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. In International Conference on Learning Representations
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. 2019 · 2019
Cited alongside, same era.
Urban traffic prediction from spatio-temporal data using deep meta learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1720–1730
Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, and Junbo Zhang. 2019 · 2019
Cited alongside, same era.
Spatio-temporal graph structure learning for traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 1177–1185
Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. 2020 · 2020
Later among the works it cites.
Gman: A graph multi-attention network for traffic prediction. In Proceedings of the AAAI Conference on Artificial Intelligence . 1234–1241
Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. 2020 · 2020
Later among the works it cites.
MDTP: A Multi-source Deep Traffic Prediction Framework over Spatio-Temporal Trajectory Data
Ziquan Fang, Lu Pan, Lu Chen, Yuntao Du, and Yunjun Gao. 2021 · 2021
Later among the works it cites.
Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting
Shengnan Guo, Youfang Lin, Huaiyu Wan, Xiucheng Li, and Gao Cong. 2021 · 2021
Later among the works it cites.
Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 547–555
Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, and Hui Xiong. 2021 · 2021
Later among the works it cites.
An Empirical Experiment on Deep Learning Models for Predicting Traffic Data. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) . IEEE, 1817–1822
Hyunwook Lee, Cheonbok Park, Seungmin Jin, Hyeshin Chu, Jaegul Choo, and Sungahn Ko. 2021 · 2021
Later among the works it cites.
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Depeng Jin, and Yong Li. 2021 · 2021
Later among the works it cites.
Artificial intelligence: A powerful paradigm for scientific research
Yongjun Xu, Xin Liu, Xin Cao, Changping Huang, Enke Liu, Sen Qian, Xingchen Liu, Yanjun Wu, Fengliang Dong, Cheng-Wei Qiu, et al · 2021
Later among the works it cites.
Coupled Layer-wise Graph Convolution for Transportation Demand Prediction. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021 . 4617–4625
Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, and Hui Xiong. 2021 · 2021
Later among the works it cites.
An effective joint prediction model for travel demands and traffic flows. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) . IEEE, 348–359
Haitao Yuan, Guoliang Li, Zhifeng Bao, and Ling Feng. 2021 · 2021
Later among the works it cites.
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021 . 11106–11115
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021 · 2021
Later among the works it cites.
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
Zezhi Shao, Zhao Zhang, Fei Wang, and Yongjun Xu. 2022 · 2022
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