Data underlying the publication: Minimising missed and false alarms: a vehicle spacing based approach to conflict detection

DOI:10.4121/252a79e7-d9ff-4181-a9e4-842ea7845a77.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/252a79e7-d9ff-4181-a9e4-842ea7845a77

Datacite citation style

Jiao, Yiru; Simeon Calvert; van Lint, Hans (2025): Data underlying the publication: Minimising missed and false alarms: a vehicle spacing based approach to conflict detection. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/252a79e7-d9ff-4181-a9e4-842ea7845a77.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This dataset includes the resulting data of the research: Minimising missed and false alarms: a vehicle spacing based approach to conflict detection. It contains processed data from the 100Car NDS, organised data from the CitySim FreewayB subset, as well as output files generated by conflict detection analyses. The research objective is to minimise missed and false alarms in vehicle conflict detection by optimising critical spacing thresholds. This study combines simulated traffic scenarios and real-world near-crashes to evaluate conflict detection strategies. Systematic data collection methods are used to compile vehicle trajectories, conflict events, and spacing distributions for comprehensive analysis. The scripts that produced these data are open-sourced at https://github.com/Yiru-Jiao/Conflict-detection-MFaM

History

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

Publisher

4TU.ResearchData

Format

HDF5, CSV

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)