TY - GEN
T1 - Geographical Stacking Point Map of Staying Points
AU - He, Jing
AU - Chen, Haonan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - Regardless of the size of the study, the trajectory contains many behaviors of moving objects in dynamic and complex environments. The advent of modern computing and software environments for integrating and manipulating trajectory data has become an important driver of mobile object trajectory research. However, understanding the behavior of moving objects is still an indirect task, as it involves not only the decision process, but also space, time, attribute constraints, and context. Therefore, the development of characterization and computational frameworks that facilitate analysis and exploration of the trajectory state of moving objects is still considered a research challenge. In this paper, we introduce a semantic behavior that expresses the trajectory stop points by the visualization technique of stacked points, in order to find a better ability to predict how trajectories respond to environmental changes and how these responses are related in time and space.
AB - Regardless of the size of the study, the trajectory contains many behaviors of moving objects in dynamic and complex environments. The advent of modern computing and software environments for integrating and manipulating trajectory data has become an important driver of mobile object trajectory research. However, understanding the behavior of moving objects is still an indirect task, as it involves not only the decision process, but also space, time, attribute constraints, and context. Therefore, the development of characterization and computational frameworks that facilitate analysis and exploration of the trajectory state of moving objects is still considered a research challenge. In this paper, we introduce a semantic behavior that expresses the trajectory stop points by the visualization technique of stacked points, in order to find a better ability to predict how trajectories respond to environmental changes and how these responses are related in time and space.
KW - moving object
KW - semantic behavior
KW - stacking point
KW - stay point
KW - trajectory data
UR - https://www.scopus.com/pages/publications/85085946194
U2 - 10.1109/ICBDA49040.2020.9101275
DO - 10.1109/ICBDA49040.2020.9101275
M3 - 会议稿件
AN - SCOPUS:85085946194
T3 - 2020 5th IEEE International Conference on Big Data Analytics, ICBDA 2020
SP - 73
EP - 79
BT - 2020 5th IEEE International Conference on Big Data Analytics, ICBDA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Big Data Analytics, ICBDA 2020
Y2 - 8 May 2020 through 11 May 2020
ER -