Research Institute of Engineering Technology, Hanyang University Erica Campus, Ansan 15588, Korea.
Department of Transportation and Logistics Engineering, Hanyang University Erica Campus, Ansan 15588, Korea.
Int J Environ Res Public Health. 2020 Dec 18;17(24):9505. doi: 10.3390/ijerph17249505.
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.
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.
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.
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.
与出租车司机健康相关的因素对于根据人为因素识别高危司机非常重要。本研究的目的是通过识别反映司机个人特征的司机健康状况，基于深度学习方法预测高危出租车司机。
对出租车司机进行深入访谈以收集健康数据。通过随机森林模型得出影响事故严重程度的因素的优先级。此外，基于得出的变量优先级，将各种输入组合设置为场景，并为每个场景推导出最佳的人工神经网络模型。最后，根据三个标准选择预测高危出租车司机的最佳性能模型。
选择了输入变量优先级最高可达 16 位的模型作为最佳模型，该模型的分类准确率为 86%，F1 得分为 0.77。
本研究提出的基于健康状况预测高危出租车司机的模型可用于开发出租车司机管理系统。此外，当为商用车司机制定定制化交通安全改进措施时，该模型也很有帮助。
