2021
DOI: 10.1016/j.aap.2021.106297
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Deriving a joint risk estimate from dynamic data collected at motorcycle rides

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Cited by 4 publications
(2 citation statements)
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“…These aspects make automatic dataset segmentation extremely valuable, as labelled and interpreted data can be used to compare and investigate specific features. More detailed data should aid in predicting individual mobility [5] and key performance indicators for ride pooling [6], and individual-and road-section-dependent riding risk [7].…”
Section: Introductionmentioning
confidence: 99%
“…These aspects make automatic dataset segmentation extremely valuable, as labelled and interpreted data can be used to compare and investigate specific features. More detailed data should aid in predicting individual mobility [5] and key performance indicators for ride pooling [6], and individual-and road-section-dependent riding risk [7].…”
Section: Introductionmentioning
confidence: 99%
“…É imprescindível destacar também que mais da metade de todas as mortes no trânsito ocorrem entre os usuários mais vulneráveis das vias, com evidência para pedestres 18,19 , ciclistas [20][21][22] e motociclistas 23 . Particularmente, no que se refere às lesões mais frequentes decorrentes dos ATT, destacam-se as fraturas [24][25][26][27] , os traumatismos cranianos 28 , os traumas raquimedulares 29 e as amputações 30,31 , acometimentos que podem induzir a morte 32 .…”
Section: Introductionunclassified