Data underlying the publication: A unified probabilistic approach to traffic conflict detection

DOI:10.4121/06415947-2b9b-4435-833e-e513ae71a6ed.v1
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DOI: 10.4121/06415947-2b9b-4435-833e-e513ae71a6ed

Datacite citation style

Jiao, Yiru; Simeon Calvert; van Cranenburgh, Sander; van Lint, Hans (2025): Data underlying the publication: A unified probabilistic approach to traffic conflict detection. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/06415947-2b9b-4435-833e-e513ae71a6ed.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This dataset includes the resulting data of the research: A unified probabilistic approach to traffic conflict detection. It contains a bundled collection of processed experimental data that excludes raw sensor recordings and initial preprocessing outputs. The study is on traffic conflict detection using a unified probabilistic approach. The highD dataset is used for lane-changing dynamics, and the 100Car NDS is used for crash and near-crash events to test the proposed approach. The raw data went through pre-processing steps that add heading directions, extract lane-change trajectories, and align crash data, enabling both conflict probability estimation and intensity evaluation. The scripts that produced these data are open-sourced at https://github.com/Yiru-Jiao/UnifiedConflictDetection

History

  • 2025-06-06 first online, published, posted

Publisher

4TU.ResearchData

Format

HDF5, CSV, PTH, PNG, MP4, GIF

Funding

  • TU Delft AI Labs programme [more info...] Delft University of Technology

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning, Traffic Systems Engineering

DATA

Files (2)