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CASIA OpenIR
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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：246/30  |  提交时间：2022/09/19
deep reinforcement learning  online ride-hailing system  hierarchical repositioning framework  parallel coordination mechanism  mixed state  
Parallel Transportation Management and Control System and Its Applications in Building Smart Cities 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 卷号: 17, 期号: 6, 页码: 1576-1585
作者:  Zhu, Fenghua;  Li, Zhenjiang;  Chen, Songhang;  Xiong, Gang
Adobe PDF(1133Kb)  |  收藏  |  浏览/下载：529/207  |  提交时间：2016/10/20
Intelligent Transportation Systems (Its)  Artificial System  Computational Experiment And Parallel Execution (Acp) Approach  Parallel Transportation Management And Control System (Ptms)  Computational Experiment  Its Cloud  Social Transportation  Transportation Knowledge Automation  
UTN-Model-Based Traffic Flow Prediction for Parallel-Transportation Management Systems 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2013, 卷号: 14, 期号: 3, 页码: 1541-1547
作者:  Kong, Qing-Jie;  Xu, Yanyan;  Lin, Shu;  Wen, Ding;  Zhu, Fenghua;  Liu, Yuncai
Adobe PDF(607Kb)  |  收藏  |  浏览/下载：361/97  |  提交时间：2015/08/12
Corsim  Fundamental Diagram (Fd)  Parallel-transportation Management Systems (Ptms)  Short-term Traffic Flow Prediction  Urban Traffic Network (Utn) Model  
Computational Traffic Experiments Based on Artificial Transportation Systems: An Application of ACP Approach 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2013, 卷号: 14, 期号: 1, 页码: 189-198
作者:  Zhu, Fenghua;  Wen, Ding;  Chen, Songhang
浏览  |  Adobe PDF(1163Kb)  |  收藏  |  浏览/下载：308/78  |  提交时间：2015/08/12
Agent  Artificial Transportation Systems (Atss)  Artificial Societies  Computational Experiment  Computational Experiments  "simple Is consIstent"  And Parallel Execution (Acp) Approach