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Dual-track spatio-temporal learning for urban flow prediction with adaptive normalization
Artificial Intelligence 328 (C):104065 (2024)
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References found in this work
Multi-view graph convolutional networks with attention mechanism.Kaixuan Yao, Jiye Liang, Jianqing Liang, Ming Li & Feilong Cao - 2022 - Artificial Intelligence 307 (C):103708.
Expanding the prediction capacity in long sequence time-series forecasting.Haoyi Zhou, Jianxin Li, Shanghang Zhang, Shuai Zhang, Mengyi Yan & Hui Xiong - 2023 - Artificial Intelligence 318 (C):103886.
Predicting citywide crowd flows using deep spatio-temporal residual networks.Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi & Tianrui Li - 2018 - Artificial Intelligence 259 (C):147-166.
AutoSTG+: An automatic framework to discover the optimal network for spatio-temporal graph prediction.Songyu Ke, Zheyi Pan, Tianfu He, Yuxuan Liang, Junbo Zhang & Yu Zheng - 2023 - Artificial Intelligence 318 (C):103899.
Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations.Yusuke Tanaka, Tomoharu Iwata, Takeshi Kurashima, Hiroyuki Toda, Naonori Ueda & Toshiyuki Tanaka - 2021 - Artificial Intelligence 292:103430.
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