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DACT
Dataset of Annotated Car Trajectories
Read me file
readme.txt
Read me file
DACT contains two subsets of annotated car trajectories data. The dataset contains 50 trajectories which cover about 13 hours of driving data. In DACT, we manually specified significant driving patterns by using an interactive framework. A significant driving pattern can be anything like a turn, speed-up, slow-down, etc. The annotation process consists of a crowd-sourcing task followed by comprehensive aggregation phases. The aggregation is done by two different strategies: Strict and Easy. For the first one, we used some strict constraints to aggregate crowd-sourcing results, while we used flexible constraints to generate the second subset of DACT. More information about this dataset may be find here: https://arxiv.org/abs/1705.05219 .

Please cite this paper "Annotation of Car Trajectories based on Driving Patterns", available at https://arxiv.org/abs/1705.05219, if you want to use this dataset.
2017 10 19
The size of this dataset is less than 10 Mb
Related publications
Other metadata
  • External Identifiers:

  • Subjects:

    Computer Science, Geography
  • Keywords:

    data-20management, spatiotemporal
  • Corresponding tasks:

    classification, regression analysis, pattern extraction, prediction, clustering, visualisation
  • Encoding data format:

    csv

Moosavi, Sobhan, Behrooz Omidvar-Tehrani and Rajiv Ramnath. DACT [data set] used in experiments for the paper "Trajectory Annotation by Discovering Driving Patterns." UrbanGIS, SIGSPATIAL, 2017, doi:10.18709/PERSCIDO.2017.10.DS144. Published 2017 via Perscido-Grenoble-Alpes;

Moosavi, Sobhan, Behrooz Omidvar-Tehrani and Rajiv Ramnath. DACT [data set] used in experiments for the paper "Trajectory Annotation by Discovering Driving Patterns." UrbanGIS, SIGSPATIAL, 2017, doi:10.18709/PERSCIDO.2017.10.DS144. Published 2017 via Perscido-Grenoble-Alpes