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CASIA OpenIR
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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：311/77  |  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, 卷号: 23, 期号: 11, 页码: 21861-21872
作者:  Xi, Jinhao;  Zhu, Fenghua;  Ye, Peijun;  Lv, Yisheng;  Tang, Haina;  Wang, Fei-Yue
Adobe PDF(3316Kb)  |  收藏  |  浏览/下载：334/46  |  提交时间：2022/09/19
deep reinforcement learning  online ride-hailing system  hierarchical repositioning framework  parallel coordination mechanism  mixed state  
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation 会议论文
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Online, 2021-6-19
作者:  Fan, Siqi;  Dong, Qiulei;  Zhu, Fenghua;  Lv, Yisheng;  Ye, Peijun;  Wang, Feiyue
Adobe PDF(4245Kb)  |  收藏  |  浏览/下载：249/44  |  提交时间：2022/06/16
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：431/139  |  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：84/41  |  提交时间：2022/04/08
Traffic control , reinforcement learning , deep learning , deep reinforcement learning  
MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity Analysis 期刊论文
IEEE Transactions on Intelligent Transportation Systems, 2021, 卷号: 0, 期号: 0, 页码: 0
作者:  Chizhan Zhang;  Fenghua Zhu;  Yisheng Lv;  Peijun Ye;  Feiyue Wang
Adobe PDF(4431Kb)  |  收藏  |  浏览/下载：265/65  |  提交时间：2021/06/16
Taxi demand prediction  taxi zone clustering  heterogeneity analysis  deep 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：308/64  |  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：338/75  |  提交时间：2020/10/15
On Iterative Proportional Updating: Limitations and Improvements for General Population Synthesis 期刊论文
IEEE Transactions on Cybernetics, 2022, 卷号: 52, 期号: 3, 页码: 1726-1735
作者:  Peijun Ye;  Bin Tian;  Yisheng Lv;  Qijie Li;  Fei-Yue Wang
Adobe PDF(1066Kb)  |  收藏  |  浏览/下载：276/58  |  提交时间：2020/10/15
Agent-based simulation, bilevel optimization, iterative proportional updating (IPU), population synthesis  
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：401/69  |  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