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dc.contributor.authorPan, Guangyuan
dc.contributor.authorFu, Liping
dc.contributor.authorThakali, Lalita 16:03:12 (GMT) 16:03:12 (GMT)
dc.descriptionThe definitive publication is available at Elsevier via © 2017. This version, has not been modified, and is made available under the CC-BY-NC-ND 4.0 license
dc.description.abstractThis paper explores the idea of applying a machine learning approach to develop a global road safety performance function (SFP) that can be used to predict the expected crash frequencies of different highways from different regions. A deep belief network (DBN) – one of the most popular deep learning models is introduced as an alternative to the traditional regression models for crash modelling. An extensive empirical study is conducted using three real world crash data sets covering six classes of highways as defined by location (urban vs. rural), number of lanes, access control, and region. The study involves a number of experiments aiming at addressing several critical questions pertaining to the relative performance of the DBN in terms of network structure, training method, data size, and generalization ability, as compared to the traditional regression models. The experimental results have shown that a DBN model could be trained with different crash datasets with prediction performance being at least comparable to that of the locally calibrated negative binomial (NB) model.en
dc.description.sponsorshipNational Science and Engineering Research Council of Canadaen
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectRoad safetyen
dc.subjectCollision modelen
dc.subjectGlobal modelen
dc.subjectDeep learningen
dc.titleDevelopment of a global road safety performance function using deep neural networksen
dcterms.bibliographicCitationPan, G., Fu, L., & Thakali, L. (2017). Development of a global road safety performance function using deep neural networks. International Journal of Transportation Science and Technology.
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation2Civil and Environmental Engineeringen

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