TY - GEN
T1 - Discovering real-time reachable area using trajectory connections
AU - Li, Ruiyuan
AU - Bao, Jie
AU - He, Huajun
AU - Ruan, Sijie
AU - He, Tianfu
AU - Hong, Liang
AU - Jiang, Zhongyuan
AU - Zheng, Yu
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Discovering real-time reachable areas of a specified location is of importance for many location-based applications. The real-time reachable area of given location changes with different environments. Existing methods fail to capture real-time traffic conditions instantly. This paper provides the first attempt to discover real-time reachable areas with real-time trajectories. To address the data sparsity issue raised by the limited real-time trajectories, we propose a trajectory connection technique, which connects sub-trajectories passing the same location. Specifically, we propose a framework that combines indexing and machine learning techniques: 1) we propose a set of indexing and query processing techniques to efficiently find reachable areas with an arbitrary number of trajectory connections; 2) we propose to predict the best number of connections in any location and at any time based on multiple datasets. Extensive experiments and one case study demonstrate the effectiveness and efficiency of our methods.
AB - Discovering real-time reachable areas of a specified location is of importance for many location-based applications. The real-time reachable area of given location changes with different environments. Existing methods fail to capture real-time traffic conditions instantly. This paper provides the first attempt to discover real-time reachable areas with real-time trajectories. To address the data sparsity issue raised by the limited real-time trajectories, we propose a trajectory connection technique, which connects sub-trajectories passing the same location. Specifically, we propose a framework that combines indexing and machine learning techniques: 1) we propose a set of indexing and query processing techniques to efficiently find reachable areas with an arbitrary number of trajectory connections; 2) we propose to predict the best number of connections in any location and at any time based on multiple datasets. Extensive experiments and one case study demonstrate the effectiveness and efficiency of our methods.
UR - https://www.scopus.com/pages/publications/85092114686
U2 - 10.1007/978-3-030-59416-9_3
DO - 10.1007/978-3-030-59416-9_3
M3 - Conference contribution
AN - SCOPUS:85092114686
SN - 9783030594152
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 36
EP - 53
BT - Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings
A2 - Nah, Yunmook
A2 - Cui, Bin
A2 - Lee, Sang-Won
A2 - Yu, Jeffrey Xu
A2 - Moon, Yang-Sae
A2 - Whang, Steven Euijong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020
Y2 - 24 September 2020 through 27 September 2020
ER -