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CASIA OpenIR
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Foundation Models for Transportation Intelligence: ITS Convergence in TransVerse 期刊论文
IEEE Intelligent Systems, 2022, 卷号: 37, 期号: 6, 页码: 77-82
作者:  Zhao, Chen;  Dai, Xingyuan;  Yisheng Lv;  Tian, Yonglin;  Ren, Yuhai;  Wang, Fei-Yue
Adobe PDF(2790Kb)  |  收藏  |  浏览/下载：35/9  |  提交时间：2024/05/28
DeCAST in TransVerse for Parallel Intelligent Transportation Systems and Smart Cities: Three Decades and Beyond 期刊论文
IEEE Intelligent Transportation Systems Magazine, 2022, 卷号: 14, 期号: 6, 页码: 6-17
作者:  Zhao, Chen;  Lv, Yisheng;  Jin, Junchen;  Tian, Yonglin;  Wang, Jiangong;  Wang, Fei-Yue
Adobe PDF(2979Kb)  |  收藏  |  浏览/下载：45/14  |  提交时间：2024/05/28
Parallel Transportation in TransVerse: From Foundation Models to DeCAST 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 卷号: 24, 期号: 12, 页码: 15310-15327
作者:  Zhao, Chen;  Wang, Xiao;  Lv, Yisheng;  Tian, Yonglin;  Lin, Yilun;  Wang, Fei-Yue
Adobe PDF(4139Kb)  |  收藏  |  浏览/下载：190/10  |  提交时间：2023/11/16
Intelligent Transportation Systems (ITS)  Cyber-Physical-Social Systems (CPSS)  Artificial Systems, Computational Experiments, Parallel Execution (ACP)  Decentralized/Distributed Autonomous Operations and Organizations (DAO)  
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：142/66  |  提交时间：2023/08/10
High-speed railway  reinforcement learning  train timetable rescheduling  Transformer  
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：305/75  |  提交时间：2023/01/03
Deep learning  graph neural network (GNN)  multi-stream  spatial-temporal feature extraction  temporal graph  traffic prediction  
Image-Based Traffic Signal Control via World Models 期刊论文
Frontiers of Information Technology & Electronic Engineering, 2022, 页码: 0
作者:  Dai, Xingyuan;  Zhao, Chen;  Wang, Xiao;  Lv, Yisheng;  Lin, Yilun;  Wang, Fei-Yue
收藏  |  浏览/下载：162/0  |  提交时间：2022/10/11
Bridging the Micro and Macro: Calibration of Agent-Based Model Using Mean-Field Dynamics 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 卷号: 52, 期号: 11, 页码: 11397-11406
作者:  Ye, Peijun;  Chen, Yuanyuan;  Zhu, Fenghua;  Lv, Yisheng;  Lu, Wanze;  Wang, Fei-Yue
收藏  |  浏览/下载：277/0  |  提交时间：2022/01/27
Calibration  Computational modeling  Mathematical model  Machine learning  Aggregates  Optimization  Bayes methods  Agent-based model (ABM)  calibration  Markovian process  
Three Principles to Determine the Right-of-Way for AVs: Safe Interaction With Humans 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 卷号: 23, 期号: 7, 页码: 16
作者:  Li, Li;  Zhao, Can;  Wang, Xiao;  Li, Zhiheng;  Chen, Long;  Lv, Yisheng;  Zheng, Nan-Ning;  Wang, Fei-Yue
收藏  |  浏览/下载：204/0  |  提交时间：2022/01/27
Vehicles  Safety  Accidents  Sensors  Prototypes  Autonomous vehicles  Automation  Autonomous vehicles  driving safety  social interaction  
FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection 期刊论文
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 卷号: 70, 期号: 1, 页码: 121-132
作者:  Fan, Siqi;  Zhu, Fenghua;  Chen, Shichao;  Zhang, Hui;  Tian, Bin;  Lv, Yisheng;  Wang, Fei-Yue
Adobe PDF(7159Kb)  |  收藏  |  浏览/下载：275/41  |  提交时间：2021/03/29
Object detection  Anchor-free detector  Foreground region proposal  
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：304/63  |  提交时间：2020/10/16
Traffic Flow Prediction  Ensemble Learning  Deep Learning