TY - GEN
T1 - Scalable Unsupervised Multi-Criteria Trajectory Segmentation and Driving Preference Mining
AU - Barth, Florian
AU - Funke, Stefan
AU - Skovgaard Jepsen, Tobias
AU - Proissl, Claudius
PY - 2020/11/3
Y1 - 2020/11/3
N2 - We present analysis techniques for large trajectory data sets that aim to provide a semantic understanding of trajectories reaching beyond them being point sequences in time and space. The presented techniques use a driving preference model w.r.t. road segment traversal costs, e.g., travel time and distance, to analyze and explain trajectories.In particular, we present trajectory mining techniques that can (a) find interesting points within a trajectory indicating, e.g., a viapoint, and (b) recover the driving preferences of a driver based on their chosen trajectory. We evaluate our techniques on the tasks of viapoint identification and personalized routing using a data set of more than 1 million vehicle trajectories collected throughout Denmark during a 3-year period. Our techniques can be implemented efficiently and are highly parallelizable, allowing them to scale to millions or billions of trajectories.
AB - We present analysis techniques for large trajectory data sets that aim to provide a semantic understanding of trajectories reaching beyond them being point sequences in time and space. The presented techniques use a driving preference model w.r.t. road segment traversal costs, e.g., travel time and distance, to analyze and explain trajectories.In particular, we present trajectory mining techniques that can (a) find interesting points within a trajectory indicating, e.g., a viapoint, and (b) recover the driving preferences of a driver based on their chosen trajectory. We evaluate our techniques on the tasks of viapoint identification and personalized routing using a data set of more than 1 million vehicle trajectories collected throughout Denmark during a 3-year period. Our techniques can be implemented efficiently and are highly parallelizable, allowing them to scale to millions or billions of trajectories.
KW - Personalized Routing
KW - Driving Preference Mining
KW - Transportation
KW - Trajectory Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85097575597&partnerID=8YFLogxK
U2 - 10.1145/3423336.3429348
DO - 10.1145/3423336.3429348
M3 - Article in proceeding
SP - 1
EP - 10
BT - BIGSPATIAL '20
A2 - Chandola, Varun
A2 - Vatsavai, Ranga Raju
A2 - Shashidharan, Ashwin
PB - Association for Computing Machinery
T2 - The 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
Y2 - 3 November 2020 through 3 November 2020
ER -