A Deep Learning Based Model for Driving Risk Assessment

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dc.contributor.authorBian, Yiyang
dc.contributor.authorLee, Chang Heon
dc.contributor.authorZhao, J Leon
dc.contributor.authorWan, Yibo
dc.date.accessioned2019-01-02T23:51:14Z
dc.date.available2019-01-02T23:51:14Z
dc.date.issued2019-01-08
dc.description.abstractIn this paper a novel multilayer model is proposed for assessing driving risk. Studying aggressive behavior via massive driving data is essential for protecting road traffic safety and reducing losses of human life and property in smart city context. In particular, identifying aggressive behavior and driving risk are multi-factors combined evaluation process, which must be processed with time and environment. For instance, improper time and environment may facilitate abnormal driving behavior. The proposed Dynamic Multilayer Model consists of identifying instant aggressive driving behavior that can be visited within specific time windows and calculating individual driving risk via Deep Neural Networks based classification algorithms. Validation results show that the proposed methods are particularly effective for identifying driving aggressiveness and risk level via real dataset of 2129 drivers’ driving behavior.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.158
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59570
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Support for Smart Cities
dc.subjectDecision Analytics, Mobile Services, and Service Science
dc.subjectdriving risk, aggressive driving behavior, deep learning
dc.titleA Deep Learning Based Model for Driving Risk Assessment
dc.typeConference Paper
dc.type.dcmiText

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