%0 Journal Article
%T Deep-learning-based prediction of high-risk taxi drivers using wellness data
%J International journal of environmental research and public health
%D 2020
%A Lee, Seolyoung
%A Kim, Jae Hun
%A Park, Jiwon
%A Oh, Cheol
%A Lee, Gunwoo
%V 17
%N 24
%P e9505-e9505
%X BACKGROUND: Factors related to the wellness of taxi drivers are important for  identifying high-risk drivers based on human factors. The purpose of this study is  to predict high-risk taxi drivers based on a deep learning method by identifying the  wellness of a driver, which reflects the personal characteristics of the driver. <br><br>METHODS: In-depth interviews with taxi drivers are conducted to collect wellness  data. The priorities of factors affecting the severity of accidents are derived  through a random forest model. In addition, based on the derived priority of  variables, various combinations of inputs are set as scenarios and optimal  artificial neural network models are derived for each scenario. Finally, the model  with the best performance for predicting high-risk taxi drivers is selected based on  three criteria. <br><br>RESULTS: A model with variables up to the 16th priority as inputs is  selected as the best model; this has a classification accuracy of 86% and an  F1-score of 0.77. <br><br>CONCLUSIONS: The wellness-based model for predicting high-risk  taxi drivers presented in this study can be used for developing a taxi driver  management system. In addition, it is expected to be useful when establishing  customized traffic safety improvement measures for commercial vehicle drivers.<p /> <p>Language: en</p>
%G en
%I MDPI: Multidisciplinary Digital Publishing Institute
%@ 1661-7827
%U http://dx.doi.org/10.3390/ijerph17249505