{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/26362"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/26362","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Identifying Functional Relationships in Driver Risk Taking: An Intelligent Transportation Assessment of Problem Behavior and Driving Style","abstract":"Intelligent transportation systems data collected on drivers who presumably participated in a study of cognitive mapping and way-finding were evaluated with two basic procedures for data coding, including analysis of video data based on the occurrence or non-occurrence of a) critical behaviors during consecutive 15 second intervals of a driving trial, and b) the safe alternative when a safe behavior opportunity was available. Methods of data coding were assessed for practical use, reliability, and sensitivity to variation in driving style. A factor analysis of at-risk driving behaviors identified a cluster of correlated driving behaviors that appeared to share a common characteristic identified as aggressive/impatient driving. The relationship between personality and driving style was also assessed. That is, analysis of the demographics and personality variables associated with the occurrence of at-risk driving behaviors revealed that driver Age and Type A personality characteristics were significant predictors of vehicle speed and following distance to the preceding vehicle. Results are discussed with regard to implications for safe driving interventions and problem behavior theory.","abstract_html":"Intelligent transportation systems data collected on drivers who presumably participated in a study of cognitive mapping and way-finding were evaluated with two basic procedures for data coding, including analysis of video data based on the occurrence or non-occurrence of a) critical behaviors during consecutive 15 second intervals of a driving trial, and b) the safe alternative when a safe behavior opportunity was available. Methods of data coding were assessed for practical use, reliability, and sensitivity to variation in driving style. A factor analysis of at-risk driving behaviors identified a cluster of correlated driving behaviors that appeared to share a common characteristic identified as aggressive/impatient driving. The relationship between personality and driving style was also assessed. That is, analysis of the demographics and personality variables associated with the occurrence of at-risk driving behaviors revealed that driver Age and Type A personality characteristics were significant predictors of vehicle speed and following distance to the preceding vehicle. Results are discussed with regard to implications for safe driving interventions and problem behavior theory.","abstract_has_math":false,"creators":["Boyce, Thomas Edward"],"institution":"Virginia Tech","degree_name":"Ph. D.","degree_level":"doctoral","degree_discipline":"Psychology","degree_department":"Psychology","school":null,"contributors":[],"advisors":[],"committee_chairs":["Geller, E. 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