TY - GEN
T1 - CPM
T2 - 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020
AU - Hu, Ying
AU - Jia, Jinping
AU - Zhao, Bin
AU - Ji, Genlin
AU - Yu, Zhaoyuan
AU - Liu, Xintao
N1 - Funding Information:
This study was supported by the National Natural Science Foundation of China under grant numbers 41971343, and RGC Early Career Scheme under grant numbers P0030875.
Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/11/3
Y1 - 2020/11/3
N2 - Group pattern mining from spatio-temporal trajectories of moving objects have gained significant attentions due to the prevalence of location-acquisition devices and tracking technologies. In this work, we propose a new group pattern, named converging, which is a group of moving objects that converge from different directions for a certain time period. Examples of convergings may include traffic jams, troop assembly, serious stampedes, and other public congregations. As a proof-of-concept, we implemented a visual analytic system CPM based on road-network constrained trajectories to detect converging events in road networks. A user-friendly interface is designed to help users gain insights into converging events from spatial and temporal aspects. Finally, we demonstrate the effectiveness and efficiency of our system by using a real dataset.
AB - Group pattern mining from spatio-temporal trajectories of moving objects have gained significant attentions due to the prevalence of location-acquisition devices and tracking technologies. In this work, we propose a new group pattern, named converging, which is a group of moving objects that converge from different directions for a certain time period. Examples of convergings may include traffic jams, troop assembly, serious stampedes, and other public congregations. As a proof-of-concept, we implemented a visual analytic system CPM based on road-network constrained trajectories to detect converging events in road networks. A user-friendly interface is designed to help users gain insights into converging events from spatial and temporal aspects. Finally, we demonstrate the effectiveness and efficiency of our system by using a real dataset.
KW - Converging Pattern
KW - Nearest Neighbour Query
KW - Road Network
UR - http://www.scopus.com/inward/record.url?scp=85097260482&partnerID=8YFLogxK
U2 - 10.1145/3397536.3422341
DO - 10.1145/3397536.3422341
M3 - Conference article published in proceeding or book
AN - SCOPUS:85097260482
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 203
EP - 206
BT - Proceedings of the 28th International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020
A2 - Lu, Chang-Tien
A2 - Wang, Fusheng
A2 - Trajcevski, Goce
A2 - Huang, Yan
A2 - Newsam, Shawn
A2 - Xiong, Li
PB - Association for Computing Machinery
Y2 - 3 November 2020 through 6 November 2020
ER -