Show simple item record

dc.contributor.advisorKondyli, Alexandra
dc.contributor.authorKummetha, Vishal Chandra Chandra
dc.date.accessioned2022-03-17T18:40:40Z
dc.date.available2022-03-17T18:40:40Z
dc.date.issued2020-08-31
dc.date.submitted2020
dc.identifier.otherhttp://dissertations.umi.com/ku:17322
dc.identifier.urihttp://hdl.handle.net/1808/32597
dc.description.abstractMathematical models of car-following, lane changing, and gap acceptance are mostly descriptive in nature and lack decision making or error tolerance. Including additional driver-related information with respect to behavior and cognitive characteristics would account for these lacking parameters and incorporate a human aspect to these models. Car-following, particularly in relation to the Intelligent Driver Model (IDM), was the primary component of this research. The major objectives of this research were to investigate how psychophysiological constructs can be modeled to replicate car-following behavior, and to correlate subjective measures of behavior with actual car-following behavior. This dissertation presents a thorough literature review into car-following models and existing driving and biobehavioral relationships that can be capitalized to improve the calibration and predicting capabilities of these models. A framework was theorized to utilize the task-capability interface to incorporate biobehavioral parameters such as cognitive workload, situation awareness, and level of activation in order to better predict changes in driving performance. Ninety drivers were recruited to validate the framework by participating in virtual scenarios within a driving simulator environment. The scenarios were created to capture all the necessary parameters by varying the situation complexity of individual tasks. A biobehavioral extension to the IDM was developed to easily calibrate predicted and observed values by grouping individual driver performance and behavioral traits. The model was validated and found to be an effective way of utilizing behavioral and performance variables to efficiently predict car-following behavior.
dc.format.extent207 pages
dc.language.isoen
dc.publisherUniversity of Kansas
dc.rightsCopyright held by the author.
dc.subjectCivil engineering
dc.subjectTransportation
dc.subjectCar-following
dc.subjectClustering
dc.subjectCognitive workload
dc.subjectDriver behavior
dc.subjectPhysiological measures
dc.subjectSituation awareness
dc.titleIncorporating Biobehavioral Architecture into Car-Following Models: A Driving Simulator Study
dc.typeDissertation
dc.contributor.cmtememberBennett, Caroline
dc.contributor.cmtememberDevos, Hannes
dc.contributor.cmtememberMulinazzi, Thomas E
dc.contributor.cmtememberSchrock, Steven D
dc.thesis.degreeDisciplineCivil, Environmental & Architectural Engineering
dc.thesis.degreeLevelPh.D.
dc.identifier.orcid0000-0001-9464-6838


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record