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pro vyhledávání: '"Wei, Tonglong"'
Spatiotemporal trajectory data is vital for web-of-things services and is extensively collected and analyzed by web-based hardware and platforms. However, issues such as service interruptions and network instability often lead to sparsely recorded tr
Externí odkaz:
http://arxiv.org/abs/2410.14281
Autor:
Lin, Yan, Wei, Tonglong, Zhou, Zeyu, Wen, Haomin, Hu, Jilin, Guo, Shengnan, Lin, Youfang, Wan, Huaiyu
Vehicle trajectories provide valuable movement information that supports various downstream tasks and powers real-world applications. A desirable trajectory learning model should transfer between different regions and tasks without retraining, thus i
Externí odkaz:
http://arxiv.org/abs/2408.15251
Recovering intermediate missing GPS points in a sparse trajectory, while adhering to the constraints of the road network, could offer deep insights into users' moving behaviors in intelligent transportation systems. Although recent studies have demon
Externí odkaz:
http://arxiv.org/abs/2404.19141
Autor:
Wei, Tonglong, Lin, Youfang, Guo, Shengnan, Lin, Yan, Huang, Yiheng, Xiang, Chenyang, Bai, Yuqing, Wan, Huaiyu
Trajectory data is essential for various applications as it records the movement of vehicles. However, publicly available trajectory datasets remain limited in scale due to privacy concerns, which hinders the development of trajectory data mining and
Externí odkaz:
http://arxiv.org/abs/2402.07369
Publikováno v:
In Expert Systems With Applications 15 March 2024 238 Part D
Autor:
Wei, Tonglong, Lin, Youfang, Guo, Shengnan, Lin, Yan, Zhao, Yiji, Jin, Xiyuan, Wu, Zhihao, Wan, Huaiyu
Publikováno v:
In Knowledge-Based Systems 25 January 2024 284
Akademický článek
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Publikováno v:
IEEE Transactions on Intelligent Transportation Systems; August 2023, Vol. 24 Issue: 8 p8650-8666, 17p