TY - GEN
T1 - Origin-destination trajectory diversity analysis
T2 - 19th IEEE International Conference on Mobile Data Management, MDM 2018
AU - He, Dan
AU - Ruan, Boyu
AU - Zheng, Bolong
AU - Zhou, Xiaofang
PY - 2018/7/13
Y1 - 2018/7/13
N2 - Given a pair of Origin-Destination (OD) locations, the set of trajectories passing from the original to destination, usually possesses the nature to reflect different traveling patterns between OD. In general, the higher diversity these trajectories have, the more various traveling behaviors and greater robustness of the connectivity can be revealed, which highly raises the value of transportation analysis towards the corresponding OD pair. Therefore, in this paper, we introduce a comprehensive and rational measure for trajectory diversity, on top of which we propose a novel query, Top-k Diversified Search (TkDS), that aims to find a set of k OD pairs among all the given OD pairs such that the trajectories traversing in-between have the highest diversity. Owing to the intrinsic characteristics of trajectory data, the computational cost for diversity is considerably high. Thus we present an efficient bounding algorithm with early termination to filter the candidates that are impossible to contribute the result. Finally, we demonstrate some case studies for trajectory diversity on real world dataset and give a comprehensive performance evaluation on the Top-k Diversified Search.
AB - Given a pair of Origin-Destination (OD) locations, the set of trajectories passing from the original to destination, usually possesses the nature to reflect different traveling patterns between OD. In general, the higher diversity these trajectories have, the more various traveling behaviors and greater robustness of the connectivity can be revealed, which highly raises the value of transportation analysis towards the corresponding OD pair. Therefore, in this paper, we introduce a comprehensive and rational measure for trajectory diversity, on top of which we propose a novel query, Top-k Diversified Search (TkDS), that aims to find a set of k OD pairs among all the given OD pairs such that the trajectories traversing in-between have the highest diversity. Owing to the intrinsic characteristics of trajectory data, the computational cost for diversity is considerably high. Thus we present an efficient bounding algorithm with early termination to filter the candidates that are impossible to contribute the result. Finally, we demonstrate some case studies for trajectory diversity on real world dataset and give a comprehensive performance evaluation on the Top-k Diversified Search.
KW - Trajectory Diversified Search
KW - Trajectory Diversity
KW - Trajectory Similarity
UR - http://www.scopus.com/inward/record.url?scp=85050826496&partnerID=8YFLogxK
U2 - 10.1109/MDM.2018.00030
DO - 10.1109/MDM.2018.00030
M3 - Article in proceeding
AN - SCOPUS:85050826496
VL - 2018-June
T3 - IEEE International Conference on Mobile Data Management (MDM)
SP - 135
EP - 144
BT - Proceedings - 2018 IEEE 19th International Conference on Mobile Data Management, MDM 2018
PB - IEEE
Y2 - 25 June 2018 through 28 June 2018
ER -