The following publications are possibly variants of this publication:
- ST-3DGMR: Spatio-temporal 3D grouped multiscale ResNet network for region-based urban traffic flow predictionRui He, Yunpeng Xiao, Xingyu Lu, Song Zhang, Yanbing Liu. isci, 624:68-93, May 2023. [doi]
- ST-3DGMR: Spatio-temporal 3D grouped multiscale ResNet network for region-based urban traffic flow predictionRui He, Yunpeng Xiao, Xingyu Lu, Song Zhang, Yanbing Liu. isci, 624:68-93, May 2023. [doi]
- Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow ForecastingXiyue Zhang, Chao Huang, Yong Xu, Lianghao Xia. CIKM 2020: 1853-1862 [doi]
- Predicting citywide crowd flows using deep spatio-temporal residual networksJunbo Zhang, Yu Zheng 0004, Dekang Qi, Ruiyuan Li, Xiuwen Yi, Tianrui Li. ai, 259:147-166, 2018. [doi]
- Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows PredictionJunbo Zhang, Yu Zheng, Dekang Qi. AAAI 2017: 1655-1661 [doi]
- Traffic Flow Forecasting Using a Spatio-temporal Bayesian Network PredictorShiliang Sun, Changshui Zhang, Yi Zhang. icann 2005: 273-278 [doi]
- An attention-based deep learning model for citywide traffic flow forecastingTao Zhou, Bo Huang 0001, Rongrong Li, Xiaoqian Liu, Zhihui Huang. digearth, 15(1):323-344, 2022. [doi]