{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/36089"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/36089","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Application of Naturalistic Truck Driving Data to Analyze and Improve Car Following Models","abstract":"This research effort aims to compare car-following models when the models are calibrated to individual drivers with the naturalistic data. The models used are the GHR, Gipps, Intelligent Driver, Velocity Difference, Wiedemann, and the Fritzsche model. This research effort also analyzes the Wiedemann car-following model using car-following periods that occur at different speeds. The Wiedemann car-following model uses thresholds to define the different regimes in car following. Some of these thresholds use a speed parameter, but others rely solely upon the difference in speed between the subject vehicle and the lead vehicle. This research effort also reconstructs the Wiedemann car-following model for truck driver behavior using the Naturalistic Truck Driving Study's (NTDS) conducted by Virginia Tech Transportation Institute. This Naturalistic data was collected by equipping 9 trucks with various sensors and a data acquisition system. This research effort also combines the Wiedemann car-following model with the GHR car-following model for trucks using The Naturalistic Truck Driving Study's (NTDS) data.","abstract_html":"This research effort aims to compare car-following models when the models are calibrated to individual drivers with the naturalistic data. The models used are the GHR, Gipps, Intelligent Driver, Velocity Difference, Wiedemann, and the Fritzsche model. This research effort also analyzes the Wiedemann car-following model using car-following periods that occur at different speeds. The Wiedemann car-following model uses thresholds to define the different regimes in car following. Some of these thresholds use a speed parameter, but others rely solely upon the difference in speed between the subject vehicle and the lead vehicle. This research effort also reconstructs the Wiedemann car-following model for truck driver behavior using the Naturalistic Truck Driving Study's (NTDS) conducted by Virginia Tech Transportation Institute. This Naturalistic data was collected by equipping 9 trucks with various sensors and a data acquisition system. This research effort also combines the Wiedemann car-following model with the GHR car-following model for trucks using The Naturalistic Truck Driving Study's (NTDS) data.","abstract_has_math":false,"creators":["Higgs, Bryan James"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Civil Engineering","degree_department":"Civil Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Abbas, Montasir M."],"committee_members":["Medina, Alejandra","Guo, Feng"],"year":2011,"date_issued":"2011-12-02","date_published":"2011-12-02","updated_at":"2026-07-22T22:19:51Z","subjects":["GHR Model","Wiedemann Model","Car Following","Naturalistic Data"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-12122011-152121"],"render_values":[{"text":"etd-12122011-152121","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/36089","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Abbas, Montasir M."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Medina, Alejandra","Guo, Feng"]},{"key":"dc:contributor.department","label":"Department","values":["Civil Engineering"]},{"key":"dc:creator","label":"Author","values":["Higgs, Bryan James"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:49:23Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:49:23Z","2012-01-03"]},{"key":"dc:date.issued","label":"Date","values":["2011-12-02"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["GHR Model","Wiedemann Model","Car Following","Naturalistic Data"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-12122011-152121"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/36089"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This research effort aims to compare car-following models when the models are calibrated to individual drivers with the naturalistic data. The models used are the GHR, Gipps, Intelligent Driver, Velocity Difference, Wiedemann, and the Fritzsche model. This research effort also analyzes the Wiedemann car-following model using car-following periods that occur at different speeds. The Wiedemann car-following model uses thresholds to define the different regimes in car following. Some of these thresholds use a speed parameter, but others rely solely upon the difference in speed between the subject vehicle and the lead vehicle. This research effort also reconstructs the Wiedemann car-following model for truck driver behavior using the Naturalistic Truck Driving Study's (NTDS) conducted by Virginia Tech Transportation Institute. This Naturalistic data was collected by equipping 9 trucks with various sensors and a data acquisition system. This research effort also combines the Wiedemann car-following model with the GHR car-following model for trucks using The Naturalistic Truck Driving Study's (NTDS) data."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Application of Naturalistic Truck Driving Data to Analyze and Improve Car Following Models"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Abbas, Montasir M."],"dc:contributor.committeemember":["Medina, Alejandra","Guo, Feng"],"dc:contributor.department":["Civil Engineering"],"dc:creator":["Higgs, Bryan James"],"dc:date.accessioned":["2014-03-14T20:49:23Z"],"dc:date.available":["2014-03-14T20:49:23Z","2012-01-03"],"dc:date.issued":["2011-12-02"],"dc:description.abstract":["This research effort aims to compare car-following models when the models are calibrated to individual drivers with the naturalistic data. The models used are the GHR, Gipps, Intelligent Driver, Velocity Difference, Wiedemann, and the Fritzsche model. This research effort also analyzes the Wiedemann car-following model using car-following periods that occur at different speeds. The Wiedemann car-following model uses thresholds to define the different regimes in car following. Some of these thresholds use a speed parameter, but others rely solely upon the difference in speed between the subject vehicle and the lead vehicle. This research effort also reconstructs the Wiedemann car-following model for truck driver behavior using the Naturalistic Truck Driving Study's (NTDS) conducted by Virginia Tech Transportation Institute. This Naturalistic data was collected by equipping 9 trucks with various sensors and a data acquisition system. This research effort also combines the Wiedemann car-following model with the GHR car-following model for trucks using The Naturalistic Truck Driving Study's (NTDS) data."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-12122011-152121"],"dc:identifier.uri":["http://hdl.handle.net/10919/36089"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["GHR Model","Wiedemann Model","Car Following","Naturalistic Data"],"dc:title":["Application of Naturalistic Truck Driving Data to Analyze and Improve Car Following Models"],"dc:type":["Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:51Z"}