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Decentralized Autonomous Operations and Organizations in TransVerse: Federated Intelligence for Smart Mobility 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2022, 卷号: 53, 期号: 4, 页码: 2062-2072
作者:  Zhao, Chen;  Dai, Xingyuan;  Lv, Yisheng;  Niu, Jinglong;  Lin, Yilun
Adobe PDF(1921Kb)  |  收藏  |  浏览/下载：200/1  |  提交时间：2023/02/22
Intelligent Transportation Systems (ITS)  Artificial Systems, Computational Experiments, Parallel Execution (ACP)  Cyber–Physical–Social Systems (CPSS)  
A Semisupervised End-to-End Framework for Transportation Mode Detection by Using GPS-Enabled Sensing Devices 期刊论文
IEEE INTERNET OF THINGS JOURNAL, 2022, 卷号: 9, 期号: 10, 页码: 7842-7852
作者:  Li, Zhishuai;  Xiong, Gang;  Wei, Zebing;  Lv, Yisheng;  Anwar, Noreen;  Wang, Fei-Yue
收藏  |  浏览/下载：206/0  |  提交时间：2022/07/25
GPS trajectory  human mobility  semi-supervised learning  transportation mode detection (TMD)  
A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 页码: 12
作者:  Jin, Junchen;  Rong, Dingding;  Zhang, Tong;  Ji, Qingyuan;  Guo, Haifeng;  Lv, Yisheng;  Ma, Xiaoliang;  Wang, Fei-Yue
收藏  |  浏览/下载：249/0  |  提交时间：2022/06/06
Roads  Predictive models  Data models  Recurrent neural networks  Generators  Computer architecture  Deep learning  Short-term link speed prediction  signalized urban networks  Wasserstein generative adversarial network  
Trip Purposes Mining From Mobile Signaling Data 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 卷号: 99, 期号: 99, 页码: 13
作者:  Li, Zhishuai;  Xiong, Gang;  Wei, Zebing;  Zhang, Yu;  Zheng, Meng;  Liu, Xiaoli;  Tarkoma, Sasu;  Huang, Min;  Lv, Yisheng;  Wu, Chuheng
Adobe PDF(3962Kb)  |  收藏  |  浏览/下载：391/73  |  提交时间：2022/01/27
Cellular networks  Trajectory  Semantics  Unsupervised learning  Supervised learning  Resource management  Public transportation  Trip purpose inference  cellular network data  latent Dirichlet allocation  travel behavior  big data