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dc.contributor.authorEngström, Johanen
dc.contributor.authorMiller, Andrewen
dc.contributor.authorHuang, Wenyanen
dc.contributor.authorSoccolich, Susanen
dc.contributor.authorMachiani, Sahar Ghanipooren
dc.contributor.authorJahangiri, Arashen
dc.contributor.authorDreger, Felixen
dc.contributor.authorde Winter, Joosten
dc.date.accessioned2019-10-28T12:03:21Zen
dc.date.available2019-10-28T12:03:21Zen
dc.date.issued2019-04en
dc.identifier.urihttp://hdl.handle.net/10919/95173en
dc.description.abstractThis report gives an overview of the main findings from the Behavior-based Predictive Safety Analytics – Pilot Study project. The main objective of the project was to investigate the possibilities of developing statistical models predicting individual driver crash involvement based on individual driving style, demographic and behavioral history variables, using large sets of naturalistic driving data. The project was designed as a pilot project with the objective of providing the basis for a future more comprehensive research effort. Based on Second Strategic Highway Research Program (SHRP2) data, a subset of behavior and crash data including 2,458 drivers was created for analysis. The data were analyzed to investigate to what extent these drivers were differentially involved in crashes and near crashes, to what extent this was associated with individual characteristics, and if it is possible to predict individual drivers’ crash and near crash involvement based on variables representing individual characteristics. The results clearly demonstrated the presence of differential crash and near crash involvement and showed significant associations between enduring personal factors and crash involvement. Moreover, logistic regression and random forest classifiers were relatively successful in predicting crash and near crash involvement based on individual characteristics, but the ability to specifically predict involvement in crashes was more limited.en
dc.format.mimetypeapplication/pdfen
dc.language.isoen_USen
dc.publisherSafe-D: Safety Through Disruptionen
dc.relation.ispartofseriesSAFE-D;02-020en
dc.rightsCreative Commons CC0 1.0 Universal Public Domain Dedicationen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.subjectcrashesen
dc.subjectdriver behavioren
dc.subjectdriver characteristicsen
dc.subjectdata miningen
dc.subjectdriving styleen
dc.subjectrisken
dc.subjectnaturalistic dataen
dc.titleBehavior-based Predictive Safety Analytics – Pilot Studyen
dc.typeReporten
dc.type.dcmitypeTexten


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Creative Commons CC0 1.0 Universal Public Domain Dedication
License: Creative Commons CC0 1.0 Universal Public Domain Dedication