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dc.creatorNathanail E.G., Prevedouros P.D., Mintu Miah M., De Melo Barros R.en
dc.date.accessioned2023-01-31T09:04:05Z
dc.date.available2023-01-31T09:04:05Z
dc.date.issued2019
dc.identifier10.1007/978-3-030-30241-2_57
dc.identifier.isbn9783030302405
dc.identifier.issn03029743
dc.identifier.urihttp://hdl.handle.net/11615/76952
dc.description.abstractVarious road safety analyses prove that cell phone usage cause driver distraction which, in turn, has become a leading cause for crashes. Various studies have focused on different cell phone operations such as hand-held or hand-free conversation, number dialing and text writing and reading and examined how they affect driving performance. Research efforts have been also placed on investigating the effects of sociodemographic characteristics on distraction and related them to the reaction of the drivers under distraction and the resulting speed, lane changes, lateral placement, deceleration, incidents and many other variables. The primary aim of this paper is to implement a decision trees approach in predicting the degree of influence of text reading on driving performance and associate it with self-reported behavioral and sociodemographic attributes. Data were based on a sample of 203 taxi drivers in Honolulu, who drove on a realistic driving simulator. Driving performance measures were collected under non-distraction and text-reading conditions. Among them, line encroachment incident and maximum driving blind time changes were used in combination with sociodemographic characteristics (gender, age, experience, educational level, race) and behavioral constructs (past behavior, behavior, behavioral beliefs, control beliefs, risk appreciation and descriptive norms) and decision trees were built. The analysis revealed that important predictors for maximum driving blind time changes are sociodemographic and past behavior attributes. The accuracy of the prediction increases in the case of line encroachment incident changes, with the addition of behavioral beliefs, control beliefs, risk appreciation, descriptive norms and past behavior. © Springer Nature Switzerland AG 2019.en
dc.language.isoenen
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85072881271&doi=10.1007%2f978-3-030-30241-2_57&partnerID=40&md5=6acf597328f891ef4223b62e446aaba3
dc.subjectAccident preventionen
dc.subjectArtificial intelligenceen
dc.subjectAutomobile driversen
dc.subjectCellular telephonesen
dc.subjectForecastingen
dc.subjectForestryen
dc.subjectMotor transportationen
dc.subjectRoads and streetsen
dc.subjectTaxicabsen
dc.subjectDistractionen
dc.subjectDriver distractionsen
dc.subjectDriving performanceen
dc.subjectDriving performance measuresen
dc.subjectRoad safetyen
dc.subjectRoad safety analysisen
dc.subjectSocio-demographic characteristicsen
dc.subjectText readingen
dc.subjectDecision treesen
dc.subjectSpringer Verlagen
dc.titlePredicting the impact of text-reading using decision treesen
dc.typeconferenceItemen


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