{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-2002"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-2002","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Integrating human psychology into car-following models for accurate response prediction: a realistic approach","abstract":"Car-following models traditionally focus on vehicle kinematics or dynamics parameters without considering human psychology. However, recent research has attempted to incorporate psychology into car-following models. Despite these efforts, there are limitations in understanding the psychological triggers that influence individual drivers' responses. To address this gap, we propose a new car-following model based on the Theory of Planned Behavior (TPB), which incorporates a human element by utilizing concepts of psychology. To gather the data pertaining to human input a questionnaire was carefully developed utilizing the Theory of Planned Behavior. Data collection and analysis were done through a survey using google forms. The data was used to measure the value of human driving behavior and cluster it into three behavioral profiles namely, defensive, neutral, and offensive. On the other hand, kinematic data was taken from the NGSIM dataset. The measured behavior and relevant kinematic data were then utilized in the new car-following model, which outperformed the baseline model in performance indexes. Therefore, our research presents a new mathematical model that incorporates both kinematics and psychological factors, based on real-world data, and yields better more accurate responses compared to the baseline models.","abstract_html":"Car-following models traditionally focus on vehicle kinematics or dynamics parameters without considering human psychology. However, recent research has attempted to incorporate psychology into car-following models. Despite these efforts, there are limitations in understanding the psychological triggers that influence individual drivers' responses. To address this gap, we propose a new car-following model based on the Theory of Planned Behavior (TPB), which incorporates a human element by utilizing concepts of psychology. To gather the data pertaining to human input a questionnaire was carefully developed utilizing the Theory of Planned Behavior. Data collection and analysis were done through a survey using google forms. The data was used to measure the value of human driving behavior and cluster it into three behavioral profiles namely, defensive, neutral, and offensive. On the other hand, kinematic data was taken from the NGSIM dataset. The measured behavior and relevant kinematic data were then utilized in the new car-following model, which outperformed the baseline model in performance indexes. Therefore, our research presents a new mathematical model that incorporates both kinematics and psychological factors, based on real-world data, and yields better more accurate responses compared to the baseline models.","abstract_has_math":false,"creators":["Khan, Faiza"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Onyango, Mbakisya","Sartipi, Mina; Osman, Osama A.; Wu, Weidong; Howell, Ashley N.","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-09-01T07:00:00Z","date_published":"2024-09-01T07:00:00Z","updated_at":"2026-07-24T05:47:13Z","subjects":["Traffic monitoring--Psychological aspects","Kinematics"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/833","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Onyango, Mbakisya","Sartipi, Mina; Osman, Osama A.; Wu, Weidong; Howell, Ashley N.","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Khan, Faiza"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-08-01T07:00:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-09-01T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Traffic monitoring--Psychological aspects","Kinematics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/833"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Civil and Chemical Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["Car-following models traditionally focus on vehicle kinematics or dynamics parameters without considering human psychology. However, recent research has attempted to incorporate psychology into car-following models. Despite these efforts, there are limitations in understanding the psychological triggers that influence individual drivers' responses. To address this gap, we propose a new car-following model based on the Theory of Planned Behavior (TPB), which incorporates a human element by utilizing concepts of psychology. To gather the data pertaining to human input a questionnaire was carefully developed utilizing the Theory of Planned Behavior. Data collection and analysis were done through a survey using google forms. The data was used to measure the value of human driving behavior and cluster it into three behavioral profiles namely, defensive, neutral, and offensive. On the other hand, kinematic data was taken from the NGSIM dataset. The measured behavior and relevant kinematic data were then utilized in the new car-following model, which outperformed the baseline model in performance indexes. Therefore, our research presents a new mathematical model that incorporates both kinematics and psychological factors, based on real-world data, and yields better more accurate responses compared to the baseline models."]},{"key":"dc:title","label":"Title","values":["Integrating human psychology into car-following models for accurate response prediction: a realistic approach"]}]}],"canonical_facts":{"dc:contributor":["Onyango, Mbakisya","Sartipi, Mina; Osman, Osama A.; Wu, Weidong; Howell, Ashley N.","College of Engineering and Computer Science"],"dc:creator":["Khan, Faiza"],"dc:date":["2023-08-01T07:00:00Z"],"dc:date.available":["2024-09-01T07:00:00Z"],"dc:description":["Dept. of Civil and Chemical Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.abstract":["Car-following models traditionally focus on vehicle kinematics or dynamics parameters without considering human psychology. However, recent research has attempted to incorporate psychology into car-following models. Despite these efforts, there are limitations in understanding the psychological triggers that influence individual drivers' responses. To address this gap, we propose a new car-following model based on the Theory of Planned Behavior (TPB), which incorporates a human element by utilizing concepts of psychology. To gather the data pertaining to human input a questionnaire was carefully developed utilizing the Theory of Planned Behavior. Data collection and analysis were done through a survey using google forms. The data was used to measure the value of human driving behavior and cluster it into three behavioral profiles namely, defensive, neutral, and offensive. On the other hand, kinematic data was taken from the NGSIM dataset. The measured behavior and relevant kinematic data were then utilized in the new car-following model, which outperformed the baseline model in performance indexes. Therefore, our research presents a new mathematical model that incorporates both kinematics and psychological factors, based on real-world data, and yields better more accurate responses compared to the baseline models."],"dc:identifier":["https://scholar.utc.edu/theses/833"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Traffic monitoring--Psychological aspects","Kinematics"],"dc:title":["Integrating human psychology into car-following models for accurate response prediction: a realistic approach"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:47:13Z"}