no code implementations • 29 Apr 2024 • Tonglong Wei, Youfang Lin, Yan Lin, Shengnan Guo, Lan Zhang, Huaiyu Wan
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.
1 code implementation • 13 Mar 2024 • Haomin Wen, Zhenjie Wei, Yan Lin, Jiyuan Wang, Yuxuan Liang, Huaiyu Wan
In this technical report, we explore the integration of LLMs and the popular academic writing tool, Overleaf, to enhance the efficiency and quality of academic writing.
no code implementations • 12 Feb 2024 • Tonglong Wei, Youfang Lin, Shengnan Guo, Yan Lin, Yiheng Huang, Chenyang Xiang, Yuqing Bai, Menglu Ya, Huaiyu Wan
In this paper, we propose a new problem to meet the practical application need, \emph{i. e.}, road network-constrained trajectory (RNTraj) generation, which can directly generate trajectories on the road network with road-related information.
1 code implementation • 11 Feb 2024 • Yan Lin, Jilin Hu, Shengnan Guo, Bin Yang, Christian S. Jensen, Youfang Lin, Huaiyu Wan
However, most methods target only one specific task and cannot be applied universally.
no code implementations • 4 Dec 2023 • Wei Chen, Huaiyu Wan, Yuting Wu, Shuyuan Zhao, Jiayaqi Cheng, Yuxin Li, Youfang Lin
Temporal knowledge graphs (TKGs) have been identified as a promising approach to represent the dynamics of facts along the timeline.
1 code implementation • 20 Oct 2023 • Shuhan Wu, Huaiyu Wan, Wei Chen, Yuting Wu, Junfeng Shen, Youfang Lin
To address these issues, we propose a novel knowledge graph reasoning approach, the Relational rUle eNhanced Graph Neural Network (RUN-GNN).
no code implementations • 3 Sep 2023 • Haomin Wen, Youfang Lin, Lixia Wu, Xiaowei Mao, Tianyue Cai, Yunfeng Hou, Shengnan Guo, Yuxuan Liang, Guangyin Jin, Yiji Zhao, Roger Zimmermann, Jieping Ye, Huaiyu Wan
An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker.
1 code implementation • 30 Jul 2023 • Xiaowei Mao, Haomin Wen, Hengrui Zhang, Huaiyu Wan, Lixia Wu, Jianbin Zheng, Haoyuan Hu, Youfang Lin
Deep neural networks based on supervised learning have emerged as the dominant model for the task because of their powerful ability to capture workers' behavior patterns from massive historical data.
no code implementations • 6 Jul 2023 • Yan Lin, Huaiyu Wan, Jilin Hu, Shengnan Guo, Bin Yang, Youfang Lin, Christian S. Jensen
Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services.
no code implementations • 19 Jun 2023 • Lixia Wu, Haomin Wen, Haoyuan Hu, Xiaowei Mao, Yutong Xia, Ergang Shan, Jianbin Zhen, Junhong Lou, Yuxuan Liang, Liuqing Yang, Roger Zimmermann, Youfang Lin, Huaiyu Wan
In this paper, we introduce \texttt{LaDe}, the first publicly available last-mile delivery dataset with millions of packages from the industry.
1 code implementation • 31 Jan 2023 • Haomin Wen, Youfang Lin, Yutong Xia, Huaiyu Wan, Qingsong Wen, Roger Zimmermann, Yuxuan Liang
Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for spatio-temporal graph (STG) forecasting.
no code implementations • 29 Jul 2022 • Yan Lin, Huaiyu Wan, Shengnan Guo, Jilin Hu, Christian S. Jensen, Youfang Lin
Spatio-temporal trajectories provide valuable information about movement and travel behavior, enabling various downstream tasks that in turn power real-world applications.
2 code implementations • 3 Apr 2020 • Chao Song, Youfang Lin, Shengnan Guo, Huaiyu Wan
Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning.
Ranked #3 on Traffic Prediction on BJTaxi
no code implementations • Asian Conference on Machine Learning 2019 • Yi Zhao, Huaiyu Wan, Jianwei Gao, Youfang Lin
Existing relation classification approaches mainly rely on exploiting external resources and background knowledge to improve the performance and ignore the correlations between entity pairs which are helpful for relation classification.
Ranked #10 on Relation Extraction on SemEval-2010 Task-8
no code implementations • CONLL 2019 • Yuze Ji, Youfang Lin, Jianwei Gao, Huaiyu Wan
Event Detection (ED) is one of the most important task in the field of information extraction.