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浏览/检索结果: 共81条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Structural analysis and robustness assessment of global LNG transport network from 2013 to 2023 期刊论文  OAI收割 OCEAN & COASTAL MANAGEMENT, 2025, 卷号: 263, 页码: 107619 作者:  Xu, Yang;  Peng, Peng;  Xie, Xiaowei;  Lu, Feng   |  收藏  |  浏览/下载：7/0  |  提交时间：2025/04/21 LNG transport network  Robustness assessment  Complex networks  Vessel trajectory data   Trajectory-User Linking via Multi-Scale Graph Attention Network 期刊论文  OAI收割 PATTERN RECOGNITION, 2025, 卷号: 158, 页码: 16 作者:  Li, Yujie;  Sun, Tao;  Shao, Zezhi;  Zhen, Yiqiang;  Xu, Yongjun   |  收藏  |  浏览/下载：9/0  |  提交时间：2024/12/06 Trajectory-user linking  Graph neural network  Trajectory classification  Spatio-temporal data mining  Check-in data   Identifying node-corridor-network of tourist flow and influencing factors using GPS big data: A case study in Gansu and Qinghai provinces, China 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2024, 卷号: 135, 页码: 104271 作者:  Zhang, Zhiyu;  Wang, Fuyuan;  Deng, Longtao   |  收藏  |  浏览/下载：3/0  |  提交时间：2024/12/20 GPS trajectory  Big data  Tourist flow  Spatial network  Gansu-Qinghai Region   An Analysis of Container Transportation Multiplex Networks from the Perspective of Shipping Company 期刊论文  OAI收割 WEB AND WIRELESS GEOGRAPHICAL INFORMATION SYSTEMS, W2GIS 2024, 2024, 卷号: 14673, 页码: 183-191 作者:  Xu, Yang;  Peng, Peng;  Claramunt, Christophe;  Lu, Feng   |  收藏  |  浏览/下载：3/0  |  提交时间：2024/10/21 Global Container Shipping Network  Complex Networks  Vessel Trajectory Data   Uncovering the multiplex network of global container shipping: Insights from shipping companies 期刊论文  OAI收割 JOURNAL OF TRANSPORT GEOGRAPHY, 2024, 卷号: 120, 页码: 103991 作者:  Xu, Yang;  Peng, Peng;  Lu, Feng;  Claramunt, Christophe   |  收藏  |  浏览/下载：12/0  |  提交时间：2024/10/21 Global container shipping  Multiplex network  Shipping company  Vessel trajectory data   Cascading failure modelling in global container shipping network using mass vessel trajectory data 期刊论文  OAI收割 RELIABILITY ENGINEERING & SYSTEM SAFETY, 2024, 卷号: 249, 页码: 110231 作者:  Xu, Yang;  Peng, Peng;  Claramunt, Christophe;  Lu, Feng;  Yan, Ran   |  收藏  |  浏览/下载：9/0  |  提交时间：2024/07/12 Port  Global Container shipping network  Cascading failure  Complex networks  Vessel trajectory data   Travel-mode inference based on GPS-trajectory data through multi-scale mixed attention mechanism 期刊论文  OAI收割 HELIYON, 2024, 卷号: 10 作者:  Pei, Xiaohui;  Yang, Xianjun;  Wang, Tao;  Ding, Zenghui;  Xu, Yang   |  收藏  |  浏览/下载：9/0  |  提交时间：2024/11/20 Travel-mode inference  GPS-trajectory data  Multi-scale convolution  Attention mechanism   Intercity freight connections in China under the view of mass truck trajectories 期刊论文  OAI收割 CITIES, 2024, 卷号: 150, 页码: 105034 作者:  Zhao, Yibo;  Cheng, Shifen;  Liu, Kang;  Zhang, Beibei;  Lu, Feng   |  收藏  |  浏览/下载：13/0  |  提交时间：2024/07/12 Freight connections  Regional integration  Urban interaction  Trajectory data mining  Temporal variation  Community structure   UAV Trajectory Optimization for Large-Scale and Low-Power Data Collection: An Attention-Reinforced Learning Scheme 期刊论文  OAI收割 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2024, 卷号: 23, 期号: 4, 页码: 3009-3024 作者:  Zhu, Yuchen;  Yang, Bo;  Liu, Min;  Li, Zhongcheng   |  收藏  |  浏览/下载：6/0  |  提交时间：2024/12/06 Autonomous aerial vehicles  Sensors  Data collection  Energy consumption  Wireless communication  Optimization  Data models  Unmanned aerial vehicle  trajectory optimization  data collection  attention model  deep reinforcement learning  LoRa   Knowledge Distillation for Travel Time Estimation 期刊论文  OAI收割 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2024, 页码: 12 作者:  Zhang, Haichao;  Zhao, Fang;  Wang, Chenxing;  Luo, Haiyong;  Xiong, Haoyu   |  收藏  |  浏览/下载：10/0  |  提交时间：2024/05/20 Computational modeling  Trajectory  Roads  Global Positioning System  Predictive models  Estimation  Context modeling  Spatial-travel time estimation  temporal data mining  knowledge distillation  deep learning