{"data":{"id":"10.4121/252a79e7-d9ff-4181-a9e4-842ea7845a77","type":"dois","attributes":{"doi":"10.4121/252a79e7-d9ff-4181-a9e4-842ea7845a77","prefix":"10.4121","suffix":"252a79e7-d9ff-4181-a9e4-842ea7845a77","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Jiao, Yiru","nameType":"Personal","givenName":"Yiru","familyName":"Jiao","nameIdentifiers":[{"nameIdentifier":"0000-0001-5009-2642","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Calvert, Simeon","nameType":"Personal","givenName":"Simeon","familyName":"Calvert","affiliation":[],"nameIdentifiers":[{"nameIdentifier":"0000-0002-1173-0071","nameIdentifierScheme":"https://orcid.org/"}]},{"name":"van Lint, Hans","nameType":"Personal","givenName":"Hans","familyName":"van Lint","nameIdentifiers":[{"nameIdentifier":"0000-0003-1493-6750","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"Data underlying the publication: Minimising missed and false alarms: a vehicle spacing based approach to conflict detection"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2025,"subjects":[{"subject":"Civil Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0905"},{"subject":"FOS: Civil engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Ground Transport","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"8801"},{"subject":"Automotive Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0902"},{"subject":"FOS: Mechanical engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"09"},{"subject":"Transport","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"88"},{"subject":"Advanced Driving Assistance System"},{"subject":"Forward Collision Warning"},{"subject":"Collision avoidance"},{"subject":"Conflict detection"},{"subject":"Vehicle spacing patterns"}],"contributors":[{"name":"TU Delft, Faculty Of Civil Engineering And Geosciences, Department Of Transport And Planning, Traffic Systems Engineering","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2025-06-06","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1109/iv55156.2024.10588396","relatedIdentifierType":"DOI"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/252a79e7-d9ff-4181-a9e4-842ea7845a77.v1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":["HDF5, CSV"],"version":"1","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"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","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"TU Delft AI Labs programme","funderName":"Delft University of 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