结合驾驶人问卷与驾驶评量分析肇事纪录之研究
| 本论文永久网址 | https://hdl.handle.net/11296/4sarxb |
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| 研究生 | 陈姿吟 |
| 研究生（外文） | Chen, Tzu-Yin |
| 论文名称 | 结合驾驶人问卷与驾驶评量分析肇事纪录之研究 |
| 论文名称（外文） | Analysis of crash records with driver questionnaires and driving assessment |
| 指导教授 | 吴宗修 |
| 学位类别 | 硕士 |
| 校院名称 | 国立阳明交通大学 |
| 系所名称 | 运输与物流管理学系 |
| 学门 | 运输服务学门 |
| 学类 | 运输管理学类 |
| 论文种类 | 学术论文 |
| 论文出本年 | 2020 |
| 毕业学年度 | 108 |
| 语文别 | 中文 |
| 论文页数 | 75 |
| 中文关键词 | 驾驶评量、驾驶行为、驾驶问卷、罗吉特回归、卜瓦松回归 |
| 外文关键词 | Driving assessment、Driving behavior、Driver questionnaires、Logit regression、Poisson regression |
中文摘要：本研究串联外商客户2016年至2019年之三套描述驾驶行为的问卷资料（驾驶适性诊断、危险感知、行车金头脑）、道路驾驶评量与事故资料并分析肇事原因、驾驶人特性与交通肇事的关联性，以厘清影响驾驶行为的因素。应用k-means分群法，依照危险感知总得分高低，将驾驶人区分为高、中低风险，分析不同风险组与驾驶人因素之关联性；此外，将事故数据库中之既有事故型态整并为追撞、擦撞、侧撞及其他，涉入事故车种则分为汽车-汽车、机车-汽车、行人及其他单一车辆事故。本研究将不存在共线性关系的变数按变数型态（二元变数、类别变数、连续变数）分别以罗吉特回归、普罗比回归、补余对数-对数回归、多项罗吉特回归、卜瓦松回归、负二项回归及线性回归建立关联性模型，分析影响涉入事故与否、受伤程度、事故件数、事故型态、涉入事故车种、驾驶风险及驾驶行为的因素，并以逐步回归排除不显著的变数。最后再以ROC曲线选择鉴别力较高的二元分类模型。研究结果发现：年龄、教育程度、三年内违规经验、驾驶适性诊断测验的压力紧张总分、危险感知测验的市区路边车辆开车门和山区路人行走中间和山区右岔路车辆驶出、行车金头脑测验科技新知分数和维护保养分数均为显著影响的因素。其中，驾驶人三年内的违规经验越多，风险越高（模型预测结果落在高风险组）。本研究亦发现某些显著变数的系数正负号与常理相悖（如：安全感知总分越高的驾驶人越易涉入汽车碰汽车的事故）。
外文摘要：This study concatenates three sets of questionnaire data describing driving behavior (i.e. behavior diagnosis test, hazard perception test, driver knowledge test), driving assessment and crash data which were derived from employees of a foreign company from 2016 to 2019. The data were analyzed for the relevance of the cause of crashes, and driver characteristics to clarify the factors that affect driving behavior. K-means clustering method was used to classify drivers into high, medium-low risks according to the total score of risk perception to analyze the relevance of driver factors and different risk groups. The crash types were merged into front-end, sideswipe, T-bone and others. The types of vehicles involved in the crash are divided into vehicle-vehicle, scooter-vehicle, pedestrians and other single-vehicle incidents. The variables that do not have collinearity are classified into variable types (binary variables, categorical variables, continuous variables) were established based on logit regression, probit regression, complementary log-log regression, multinomial logit regression, Poisson regression, negative binomial regression and linear regression to analyze the factor whether the driver is involved in a crash, crash severity (whether injured), the number of crashes, the type of crashes, the types of vehicles involved in a crash, driving risk and driving behavior. The stepwise regression is used to exclude insignificant variables. Finally, the ROC curve is used to select a binary classification model with higher discriminatory power. The results of the study found that age, education level, violation experience in three years, total stress score of behavior diagnosis test, urban roadside vehicle driving door of hazard perception test, mountain passers-by walking in the middle, and mountainous right road vehicle driving out and the technology knowledge score and the maintenance score of the driver knowledge test are all significant factors. Among them, the more violation experience the driver has within three years, the higher the risk (the model prediction results fall in the high risk group). This study also found that the coefficients of some significant variables are contrary to common sense. For example, drivers with higher safety perception scores are more likely to be involved in a vehicle-to-vehicle crash.