6 citations · 24 across the 26 of their papers we have counts for
17 papers · 1 filter
VaryST: Generalizable Spatio-Temporal Forecasting Beyond the Closed-World Setting
Shengnan Guo, Jinjun Cheng, Xiaowei Mao +7
Spatio-temporal forecasting is fundamental to many real-world systems, yet deployment environments are inherently varying: temporal distributions may evolve, spatial domains may ch…
DiSGMM: A Method for Time-varying Microscopic Weight Completion on Road Networks
Yan Lin, Jilin Hu, Shengnan Guo +3
Microscopic road-network weights represent fine-grained, time-varying traffic conditions obtained from individual vehicles. An example is travel speeds associated with road segment…
RIPCN: A Road Impedance Principal Component Network for Probabilistic Traffic Flow Forecasting
Haochen Lv, Yan Lin, Shengnan Guo +5
Accurate traffic flow forecasting is crucial for intelligent transportation services such as navigation and ride-hailing. In such applications, uncertainty estimation in forecastin…
TrajMamba: An Efficient and Semantic-rich Vehicle Trajectory Pre-training Model
Yichen Liu, Yan Lin, Shengnan Guo +3
Vehicle GPS trajectories record how vehicles move over time, storing valuable travel semantics, including movement patterns and travel purposes. Learning travel semantics effective…
TransferTraj: A Vehicle Trajectory Learning Model for Region and Task Transferability
Tonglong Wei, Yan Lin, Zeyu Zhou +6
Vehicle GPS trajectories provide valuable movement information that supports various downstream tasks and applications. A desirable trajectory learning model should be able to tran…
A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Models
Shengnan Guo, Tonglong Wei, Yiheng Huang +6
Traffic data imputation is a critical preprocessing step in intelligent transportation systems, underpinning the reliability of downstream transportation services. Despite substant…
Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Models
Letian Gong, Yan Lin, Xinyue Zhang +6
Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users' in…
PLMTrajRec: A Scalable and Generalizable Trajectory Recovery Method with Pre-trained Language Models
Tonglong Wei, Yan Lin, Youfang Lin +5
Spatiotemporal trajectory data is crucial for various applications. However, issues such as device malfunctions and network instability often cause sparse trajectories, leading to…
PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting
Yuxin Jia, Youfang Lin, Jing Yu +3
Due to the recurrent structure of RNN, the long information propagation path poses limitations in capturing long-term dependencies, gradient explosion/vanishing issues, and ineffic…
PTrajM: Efficient and Semantic-rich Trajectory Learning with Pretrained Trajectory-Mamba
Yan Lin, Yichen Liu, Zeyu Zhou +5
Vehicle trajectories provide crucial movement information for various real-world applications. To better utilize vehicle trajectories, it is essential to develop a trajectory learn…
Spatial-Temporal Cross-View Contrastive Pre-training for Check-in Sequence Representation Learning
Letian Gong, Huaiyu Wan, Shengnan Guo +6
The rapid growth of location-based services (LBS) has yielded massive amounts of data on human mobility. Effectively extracting meaningful representations for user-generated check-…
UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings
Yan Lin, Zeyu Zhou, Yicheng Liu +8
Spatiotemporal trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map tr…
STD-PLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal Data with PLM
YiHeng Huang, Xiaowei Mao, Shengnan Guo +5
Spatial-temporal forecasting and imputation are important for real-world intelligent systems. Most existing methods are tailored for individual forecasting or imputation tasks but…
TrajCogn: Leveraging LLMs for Cognizing Movement Patterns and Travel Purposes from Trajectories
Zeyu Zhou, Yan Lin, Haomin Wen +5
Spatio-temporal trajectories are crucial in various data mining tasks. It is important to develop a versatile trajectory learning method that performs different tasks with high acc…
Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery
Tonglong Wei, Youfang Lin, Yan Lin +3
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…
Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation
Tonglong Wei, Youfang Lin, Shengnan Guo +5
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…
UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
Yan Lin, Jilin Hu, Shengnan Guo +4
Vehicle movement is frequently captured in the form of GPS trajectories, i.e., sequences of timestamped GPS locations. Such data is widely used for various tasks such as travel-tim…