Scalable Unsupervised Multi-Criteria Trajectory Segmentation and Driving Preference Mining

Florian Barth*, Stefan Funke*, Tobias Skovgaard Jepsen*, Claudius Proissl*

*Corresponding author

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Abstract

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.
Original languageEnglish
Title of host publicationBIGSPATIAL '20: Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
EditorsVarun Chandola, Ranga Raju Vatsavai, Ashwin Shashidharan
Number of pages10
PublisherAssociation for Computing Machinery
Publication date3 Nov 2020
Pages1-10
Article number6
ISBN (Electronic)9781450381628
DOIs
Publication statusPublished - 3 Nov 2020
EventThe 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data -
Duration: 3 Nov 20203 Nov 2020
https://bigspatial2020.github.io/

Conference

ConferenceThe 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
Period03/11/202003/11/2020
Internet address

Keywords

  • Personalized Routing
  • Driving Preference Mining
  • Transportation
  • Trajectory Segmentation

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