中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共17条，第1-10条 帮助

限定条件    
已选(0)清除 条数/页：   排序方式：
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：229/29  |  提交时间：2022/09/19
deep reinforcement learning  online ride-hailing system  hierarchical repositioning framework  parallel coordination mechanism  mixed state  
Data Augmented Deep Behavioral Cloning for Urban Traffic Control Operations Under a Parallel Learning Framework 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 卷号: 23, 期号: 6, 页码: 5128-5137
作者:  Li, Xiaoshuang;  Ye, Peijun;  Jin, Junchen;  Zhu, Fenghua;  Wang, Fei-Yue
Adobe PDF(2319Kb)  |  收藏  |  浏览/下载：266/51  |  提交时间：2022/01/27
Generative adversarial networks  Data models  Gallium nitride  Task analysis  Complex systems  Intelligent traffic signal operations  deep behavioral cloning  
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：249/54  |  提交时间：2020/10/16
Traffic Flow Prediction  Ensemble Learning  Deep Learning  
Detecting Traffic Information From Social Media Texts With Deep Learning Approaches 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 卷号: 20, 期号: 8, 页码: 3049-3058
作者:  Chen, Yuanyuan;  Lv, Yisheng;  Wang, Xiao;  Li, Lingxi;  Wang, Fei-Yue
浏览  |  Adobe PDF(2273Kb)  |  收藏  |  浏览/下载：393/102  |  提交时间：2019/08/28
Deep learning  social transportation  traffic information detection  social media  text mining  
Capturing Car-Following Behaviors by Deep Learning 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2018, 卷号: 19, 期号: 3, 页码: 910-920
作者:  Wang, Xiao;  Jiang, Rui;  Li, Li;  Lin, Yilun;  Zheng, Xinhu;  Wang, Fei-Yue
浏览  |  Adobe PDF(3543Kb)  |  收藏  |  浏览/下载：423/165  |  提交时间：2018/10/10
Microscopic Car-following Model  Deep Learning  Recurrent Neural Network (Rnn)  Gated Recurrent Unit (Gru) Neural Networks  
Parking Like a Human: A Direct Trajectory Planning Solution 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2017, 卷号: 18, 期号: 12, 页码: 3388-3397
作者:  Liu, Wei;  Li, Zhiheng;  Li, Li;  Wang, Fei-Yue
浏览  |  Adobe PDF(2375Kb)  |  收藏  |  浏览/下载：373/153  |  提交时间：2018/01/04
Autonomous Vehicles  Parking  Trajectory Planning  
An Incidental Delivery Based Method for Resolving Multirobot Pairwised Transportation Problems 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 卷号: 17, 期号: 7, 页码: 1852-1866
作者:  Liu, Zhe;  Wang, Hesheng;  Chen, Weidong;  Yu, Junzhi;  Chen, Jian
Adobe PDF(3291Kb)  |  收藏  |  浏览/下载：301/82  |  提交时间：2016/10/20
Incidental Delivery  Multirobot Pairwised Transportation (Mrpwt)  Simulated Annealing  
Big Data for Social Transportation 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 卷号: 17, 期号: 3, 页码: 620-630
作者:  Zheng, Xinhu;  Chen, Wei;  Wang, Pu;  Shen, Dayong;  Chen, Songhang;  Wang, Xiao;  Zhang, Qingpeng;  Yang, Liuqing
浏览  |  Adobe PDF(1913Kb)  |  收藏  |  浏览/下载：743/266  |  提交时间：2016/06/14
Big Data  Social Transportation  Intelligent Transportation System  Data Analytics  Crowdsourcing  
A Survey of Traffic Data Visualization 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, 卷号: 16, 期号: 6, 页码: 2970-2984
作者:  Chen, Wei;  Guo, Fangzhou;  Wang, Fei-Yue
Adobe PDF(3269Kb)  |  收藏  |  浏览/下载：830/439  |  提交时间：2016/01/18
Traffic  Traffic Data Visualization  Visual Analysis  Data-driven Intelligent Transportation System  
A Security and Privacy Review of VANETs 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, 卷号: 16, 期号: 6, 页码: 2985-2996
作者:  Qu, Fengzhong;  Wu, Zhihui;  Wang, Fei-Yue;  Cho, Woong
Adobe PDF(1031Kb)  |  收藏  |  浏览/下载：532/230  |  提交时间：2016/01/18
Vanets  Security  Privacy  Survey