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Complex spatial dependencies in transportation networks make traffic prediction extremely challenging.
“Graph wavenet for deep spatial-temporal graph modeling,”
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang, · 1913
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
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“Long short-term memory neural network for traffic speed prediction using remote microwave sensor data,”
Xiaolei Ma, Zhimin Tao, Yinhai Wang, Haiyang Yu, and Yunpeng Wang, · 2015
Earlier work this paper cites.
“Applications of computational intelligence in vehicle traffic congestion problem: a survey,”
Mohammad Reza Jabbarpour, Houman Zarrabi, Rashid Hafeez Khokhar, Shahaboddin Shamshirband, and Kim-Kwang Raymond Choo, · 2018
Earlier work this paper cites.
“Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,”
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu, · 2018
Earlier work this paper cites.
“Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting,”
Bing Yu, Haoteng Yin, and Zhanxing Zhu, · 2018
Earlier work this paper cites.
“A visual analytics system for exploring, monitoring, and forecasting road traffic congestion,”
Chunggi Lee, Yeonjun Kim, Seungmin Jin, Dongmin Kim, Ross Maciejewski, David S. Ebert, and Sungahn Ko, · 2020
Earlier work this paper cites.
“Adaptive graph convolutional recurrent network for traffic forecasting,”
Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang, · 2020
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“Connecting the dots: Multivariate time series forecasting with graph neural networks,”
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang, · 2020
Cited alongside, same era.
“Artificial intelligence: A powerful paradigm for scientific research,”
Yongjun Xu, Xin Liu, Xin Cao, and et al., · 2021
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“Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting,”
Shengnan Guo, Youfang Lin, Huaiyu Wan, Xiucheng Li, and Gao Cong, · 2021
Cited alongside, same era.
“Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting,”
Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, and Hui Xiong, · 2021
Cited alongside, same era.
“Pgcn: Progressive graph convolutional networks for spatial-temporal traffic forecasting,”
Yuyol Shin and Yoonjin Yoon, · 2022
Later among the works it cites.
“Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting,”
Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, and Pyang Li, · 2022
Later among the works it cites.
“Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting,”
Zezhi Shao, Zhao Zhang, Fei Wang, Wei Wei, and Yongjun Xu, · 2022
Later among the works it cites.
“Decoupled dynamic spatial-temporal graph neural network for traffic forecasting,”
Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, and Christian S. Jensen, · 2022
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“Dsformer: A double sampling transformer for multivariate time series long-term prediction,”
Chengqing Yu, Fei Wang, Zezhi Shao, Tao Sun, Lin Wu, and Yongjun Xu, · 2023
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“Spatial-temporal fusion graph neural networks for traffic flow forecasting,”
Mengzhang Li and Zhanxing Zhu, · 2021
Cited alongside, same era.
“Historical inertia: A neglected but powerful baseline for long sequence time-series forecasting,”
Yue Cui, Jiandong Xie, and Kai Zheng, · 2021
Cited alongside, same era.
Closest in time.
“Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution,”
Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Fan Yang, Funing Sun, Depeng Jin, and Yong Li, · 2023
Closest in time.