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CASIA OpenIR
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Transformer-Based Macroscopic Regulation for High-Speed Railway Timetable Rescheduling 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 9, 页码: 1822-1833
作者:  Wei Xu;  Chen Zhao;  Jie Cheng;  Yin Wang;  Yiqing Tang;  Tao Zhang;  Zhiming Yuan;  Yisheng Lv;  Fei-Yue Wang
Adobe PDF(3484Kb)  |  收藏  |  浏览/下载：106/53  |  提交时间：2023/08/10
High-speed railway  reinforcement learning  train timetable rescheduling  Transformer  
Parallel Learning: Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 3, 页码: 603-631
作者:  Qinghai Miao;  Yisheng Lv;  Min Huang;  Xiao Wang;  Fei-Yue Wang
Adobe PDF(11937Kb)  |  收藏  |  浏览/下载：860/145  |  提交时间：2023/03/02
Machine learning  parallel learning  parallel systems  sim-to-real  syn-to-real  virtual-to-real  
STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 1, 页码: 226-238
作者:  Zebing Wei;  Hongxia Zhao;  Zhishuai Li;  Xiaojie Bu;  Yuanyuan Chen;  Xiqiao Zhang;  Yisheng Lv;  Fei-Yue Wang
Adobe PDF(7068Kb)  |  收藏  |  浏览/下载：257/65  |  提交时间：2023/01/03
Deep learning  graph neural network (GNN)  multi-stream  spatial-temporal feature extraction  temporal graph  traffic prediction  
HMDRL: Hierarchical Mixed Deep Reinforcement Learning to Balance Vehicle Supply and Demand 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 页码: 12
作者:  Xi, Jinhao;  Zhu, Fenghua;  Ye, Peijun;  Lv, Yisheng;  Tang, Haina;  Wang, Fei-Yue
Adobe PDF(3316Kb)  |  收藏  |  浏览/下载：263/30  |  提交时间：2022/09/19
deep reinforcement learning  online ride-hailing system  hierarchical repositioning framework  parallel coordination mechanism  mixed state  
AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks 期刊论文
Transportation Research Part C, 2022, 期号: 99, 页码: 1-1
作者:  Wei Zhang;  Fenghua Zhu;  Yisheng Lv;  Chang Tan;  Wen Liu;  Xin Zhang;  Fei-Yue Wang
Adobe PDF(2619Kb)  |  收藏  |  浏览/下载：371/123  |  提交时间：2022/04/08
Adaptive graph learning, Traffic prediction, Graph convolutional network, Expectation maximization, Deep learning  
Traffic Signal Timing via Deep Reinforcement Learning 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2016, 期号: 3, 页码: 247-254
作者:  Li Li;  Lv YS(吕宜生);  Fei-Yue Wang
Adobe PDF(509Kb)  |  收藏  |  浏览/下载：73/36  |  提交时间：2022/04/08
Traffic control , reinforcement learning , deep learning , deep reinforcement learning  
Acting As A Decision Maker: Traffic-Condition-Aware Ensemble Learning for Traffic Flow Prediction 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2020, 期号: Accepted, 页码: Accepted
作者:  Yuanyuan Chen;  Hongyu Chen;  Peijun Ye;  Yisheng Lv;  Fei-Yue Wang
浏览  |  Adobe PDF(2382Kb)  |  收藏  |  浏览/下载：273/57  |  提交时间：2020/10/16
Traffic Flow Prediction  Ensemble Learning  Deep Learning  
A Hybrid Deep Learning Approach with GCN and LSTM for Traffic Flow Prediction 会议论文
, Auckland, New Zealand, 2019-10-27
作者:  Zhishuai Li;  Gang Xiong;  Yuanyuan Chen;  Yisheng Lv;  Bin Hu;  Fenghua Zhu;  Fei-Yue Wang
Adobe PDF(305Kb)  |  收藏  |  浏览/下载：298/65  |  提交时间：2020/10/15
Parallel Transportation Systems: Toward IoT-Enabled Smart Urban Traffic Control and Management 期刊论文
IEEE Transactions on Intelligent Transportation Systems, 2020, 卷号: 21, 期号: 10, 页码: 4063 - 4071
作者:  Fenghua Zhu;  Yisheng Lv;  Yuanyuan Chen;  Xiao Wang;  Gang Xiong;  Fei-Yue Wang
浏览  |  Adobe PDF(2879Kb)  |  收藏  |  浏览/下载：447/227  |  提交时间：2020/10/15
Intelligent transportation systems  
Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2020, 卷号: 21, 期号: 4, 页码: 1624-1630
作者:  Chen, Yuanyuan;  Lv, Yisheng;  Wang, Fei-Yue
浏览  |  Adobe PDF(2070Kb)  |  收藏  |  浏览/下载：365/65  |  提交时间：2020/06/02
Generators  Data models  Gallium nitride  Generative adversarial networks  Training  Loss measurement  Biological system modeling  Parallel data  generative adversarial networks  traffic flow imputation  data augmentation  deep learning