Reconstructing Movement from Sparse Samples: Enhanced Spatio-Temporal Matching Strategies for Low-Frequency Data
MOX, Department of Mathematics, Politecnico di Milano（数学系，米兰理工学院MOX部门）；Modeling and Simulation of Socio-Technical Systems, Fondazione Bruno Kessler（社会技术系统建模与仿真，布鲁诺·凯瑟勒基金会）
This paper explores potential improvements to the Spatial-Temporal Matching algorithm for aligning the GPS trajectories to road networks. While this algorithm is effective, it presents some limitations in computational efficiency and the accuracy of the results, especially in dense environments with relatively high sampling intervals. To address this, the paper proposes four modifications to the original algorithm: a dynamic buffer, an adaptive observation probability, a redesigned temporal scoring function, and a behavioral analysis to account for the historical mobility patterns. The enhancements are assessed using real-world data from the urban area of Milan, and through newly defined evaluation metrics to be applied in the absence of ground truth. The results of the experiment show significant improvements in performance efficiency and path quality across various metrics.
Reconstructing GRACE Terrestrial Water Storage with Spatio-Temporal Graph Neural Networks: An Application to South America
HafenCity University, Computational Methods Lab（哈芬城大学计算方法实验室）；HafenCity University, Geodesy and Adjustment Theory（哈芬城大学大地测量与平差理论系）；GFZ Helmholtz Centre for Geosciences, Global Geomonitoring and Gravity Field（亥姆霍兹地球科学研究中心全球地球监测与重力场部）
KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy
S-Lab, Nanyang Technological University（新加坡南洋理工大学S-Lab）；University of Cologne（科隆大学）；Beijing Institute of Technology（北京理工大学）；SenseTime Research（商汤科技研究院）
