TY - GEN
T1 - Deep representation learning for trajectory similarity computation
AU - Li, Xiucheng
AU - Zhao, Kaiqi
AU - Cong, Gao
AU - Jensen, Christian Søndergaard
AU - Wei, Wei
PY - 2018/10/24
Y1 - 2018/10/24
N2 - Trajectory similarity computation is fundamental functionality with many applications such as animal migration pattern studies and vehicle trajectory mining to identify popular routes and similar drivers. While a trajectory is a continuous curve in some spatial domain, e.g., 2D Euclidean space, trajectories are often represented by point sequences. Existing approaches that compute similarity based on point matching suffer from the problem that they treat two different point sequences differently even when the sequences represent the same trajectory. This is particularly a problem when the point sequences are non-uniform, have low sampling rates, and have noisy points. We propose the first deep learning approach to learning representations of trajectories that is robust to low data quality, thus supporting accurate and efficient trajectory similarity computation and search. Experiments show that our method is capable of higher accuracy and is at least one order of magnitude faster than the state-of-The-Art methods for k-nearest trajectory search.
AB - Trajectory similarity computation is fundamental functionality with many applications such as animal migration pattern studies and vehicle trajectory mining to identify popular routes and similar drivers. While a trajectory is a continuous curve in some spatial domain, e.g., 2D Euclidean space, trajectories are often represented by point sequences. Existing approaches that compute similarity based on point matching suffer from the problem that they treat two different point sequences differently even when the sequences represent the same trajectory. This is particularly a problem when the point sequences are non-uniform, have low sampling rates, and have noisy points. We propose the first deep learning approach to learning representations of trajectories that is robust to low data quality, thus supporting accurate and efficient trajectory similarity computation and search. Experiments show that our method is capable of higher accuracy and is at least one order of magnitude faster than the state-of-The-Art methods for k-nearest trajectory search.
KW - Deep neural nets
KW - representation learning
KW - Trajectory similarity
UR - http://www.scopus.com/inward/record.url?scp=85057070863&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2018.00062
DO - 10.1109/ICDE.2018.00062
M3 - Article in proceeding
AN - SCOPUS:85057070863
SN - 978-1-5386-5520-7
T3 - Proceedings of the International Conference on Data Engineering
SP - 617
EP - 628
BT - IEEE International Conference on Data Engineering (ICDE)
PB - IEEE
T2 - 34th IEEE International Conference on Data Engineering, ICDE 2018
Y2 - 16 April 2018 through 19 April 2018
ER -