Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting H Peng, H Wang, B Du, MZA Bhuiyan, H Ma, J Liu, L Wang, Z Yang, L Du, ... Information Sciences 521, 277-290, 2020 | 310 | 2020 |
Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction B Du, H Peng, S Wang, MZA Bhuiyan, L Wang, Q Gong, L Liu, J Li IEEE Transactions on Intelligent Transportation Systems 21 (3), 972-985, 2019 | 267 | 2019 |
Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting L Han, B Du, L Sun, Y Fu, Y Lv, H Xiong Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 224 | 2021 |
Parallel architecture of convolutional bi-directional LSTM neural networks for network-wide metro ridership prediction X Ma, J Zhang, B Du, C Ding, L Sun IEEE Transactions on Intelligent Transportation Systems 20 (6), 2278-2288, 2018 | 179 | 2018 |
Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning H Peng, B Du, M Liu, M Liu, S Ji, S Wang, X Zhang, L He Information Sciences 578, 401-416, 2021 | 178 | 2021 |
Deep spatio-temporal graph convolutional network for traffic accident prediction L Yu, B Du, X Hu, L Sun, L Han, W Lv Neurocomputing 423, 135-147, 2021 | 170 | 2021 |
Co-prediction of multiple transportation demands based on deep spatio-temporal neural network J Ye, L Sun, B Du, Y Fu, X Tong, H Xiong Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 147 | 2019 |
Coupled layer-wise graph convolution for transportation demand prediction J Ye, L Sun, B Du, Y Fu, H Xiong Proceedings of the AAAI conference on artificial intelligence 35 (5), 4617-4625, 2021 | 144 | 2021 |
Dynamic spatial-temporal representation learning for traffic flow prediction L Liu, J Zhen, G Li, G Zhan, Z He, B Du, L Lin IEEE Transactions on Intelligent Transportation Systems 22 (11), 7169-7183, 2020 | 132 | 2020 |
Dynamic pricing in spatial crowdsourcing: A matching-based approach Y Tong, L Wang, Z Zhou, L Chen, B Du, J Ye Proceedings of the 2018 international conference on management of data, 773-788, 2018 | 123 | 2018 |
Estimation of missing values in heterogeneous traffic data: Application of multimodal deep learning model L Li, B Du, Y Wang, L Qin, H Tan Knowledge-Based Systems 194, 105592, 2020 | 93 | 2020 |
Adaptive spatio-temporal graph neural network for traffic forecasting X Ta, Z Liu, X Hu, L Yu, L Sun, B Du Knowledge-based systems 242, 108199, 2022 | 84 | 2022 |
S-net: From answer extraction to answer synthesis for machine reading comprehension C Tan, F Wei, N Yang, B Du, W Lv, M Zhou Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 84 | 2018 |
LSTM variants meet graph neural networks for road speed prediction Z Lu, W Lv, Y Cao, Z Xie, H Peng, B Du Neurocomputing 400, 34-45, 2020 | 83 | 2020 |
Learning the evolutionary and multi-scale graph structure for multivariate time series forecasting J Ye, Z Liu, B Du, L Sun, W Li, Y Fu, H Xiong Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and …, 2022 | 79 | 2022 |
Catch me if you can: Detecting pickpocket suspects from large-scale transit records B Du, C Liu, W Zhou, Z Hou, H Xiong Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016 | 78 | 2016 |
Attentive crowd flow machines L Liu, R Zhang, J Peng, G Li, B Du, L Lin Proceedings of the 26th ACM international conference on Multimedia, 1553-1561, 2018 | 75 | 2018 |
Sequential graph neural network for urban road traffic speed prediction Z Xie, W Lv, S Huang, Z Lu, B Du, R Huang IEEE Access 8, 63349-63358, 2019 | 74 | 2019 |
Real-time traffic incident detection based on a hybrid deep learning model L Li, Y Lin, B Du, F Yang, B Ran Transportmetrica A: transport science 18 (1), 78-98, 2022 | 72 | 2022 |
Structure sensitive hashing with adaptive product quantization X Liu, B Du, C Deng, M Liu, B Lang IEEE transactions on cybernetics 46 (10), 2252-2264, 2015 | 72 | 2015 |