Naturalistic Driving Data for the Analysis of Car-Following Models

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dc.contributor.authorSangster, John Daviden
dc.contributor.committeechairRakha, Hesham A.en
dc.contributor.committeememberHancock, Kathleen L.en
dc.contributor.committeememberDu, Jianheen
dc.contributor.departmentCivil Engineeringen
dc.date.accessioned2017-04-04T19:50:21Zen
dc.date.adate2012-01-12en
dc.date.available2017-04-04T19:50:21Zen
dc.date.issued2011-12-05en
dc.date.rdate2016-10-18en
dc.date.sdate2011-12-19en
dc.description.abstractThe driver-specific data from a naturalistic driving study provides car-following events in real-world driving situations, while additionally providing a wealth of information about the participating drivers. Reducing a naturalistic database into finite car-following events requires significant data reduction, validation, and calibration, often using manual procedures. The data collection performed herein included: the identification of commuting routes used by multiple drivers, the extraction of data along those routes, the identification of potential car-following events from the dataset, the visual validation of each car-following event, and the extraction of pertinent information from the database during each event identified. This thesis applies the developed process to generate car-following events from the 100-Car Study database, and applies the dataset to analyze four car-following models. The Gipps model was found to perform best for drivers with greater amounts of data in congested driving conditions, while the Rakha-Pasumarthy-Adjerid (RPA) model was best for drivers in uncongested conditions. The Gipps model was found to generate the lowest error value in aggregate, with the RPA model error 21 percent greater, and the Gaxis-Herman-Rothery model (GHR) and the Intelligent Driver Model (IDM) errors 143 percent and 86 percent greater, respectively. Additionally, the RPA model provides the flexibility for a driver to change vehicles without the need to recalibrate parameter values for that driver, and can also capture changes in roadway surface type and condition. With the error values close between the RPA and Gipps models, the additional advantages of the RPA model make it the recommended choice for simulation.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-12192011-105714en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12192011-105714/en
dc.identifier.urihttp://hdl.handle.net/10919/76925en
dc.language.isoen_USen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjecttraffic modelingen
dc.subjecttraffic flow theoryen
dc.subjectnaturalistic driver behavioren
dc.subjectcar-following modelingen
dc.titleNaturalistic Driving Data for the Analysis of Car-Following Modelsen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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