結合駕駛人問卷與駕駛評量分析肇事紀錄之研究
| 本論文永久網址 | https://hdl.handle.net/11296/4sarxb |
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| 研究生 | 陳姿吟 |
| 研究生（外文） | Chen, Tzu-Yin |
| 論文名稱 | 結合駕駛人問卷與駕駛評量分析肇事紀錄之研究 |
| 論文名稱（外文） | Analysis of crash records with driver questionnaires and driving assessment |
| 指導教授 | 吳宗修 |
| 學位類別 | 碩士 |
| 校院名稱 | 國立陽明交通大學 |
| 系所名稱 | 運輸與物流管理學系 |
| 學門 | 運輸服務學門 |
| 學類 | 運輸管理學類 |
| 論文種類 | 學術論文 |
| 論文出本年 | 2020 |
| 畢業學年度 | 108 |
| 語文別 | 中文 |
| 論文頁數 | 75 |
| 中文關鍵詞 | 駕駛評量、駕駛行為、駕駛問卷、羅吉特迴歸、卜瓦松迴歸 |
| 外文關鍵詞 | Driving assessment、Driving behavior、Driver questionnaires、Logit regression、Poisson regression |
中文摘要：本研究串聯外商客戶2016年至2019年之三套描述駕駛行為的問卷資料（駕駛適性診斷、危險感知、行車金頭腦）、道路駕駛評量與事故資料並分析肇事原因、駕駛人特性與交通肇事的關聯性，以釐清影響駕駛行為的因素。應用k-means分群法，依照危險感知總得分高低，將駕駛人區分為高、中低風險，分析不同風險組與駕駛人因素之關聯性；此外，將事故資料庫中之既有事故型態整併為追撞、擦撞、側撞及其他，涉入事故車種則分為汽車-汽車、機車-汽車、行人及其他單一車輛事故。本研究將不存在共線性關係的變數按變數型態（二元變數、類別變數、連續變數）分別以羅吉特迴歸、普羅比迴歸、補餘對數-對數迴歸、多項羅吉特迴歸、卜瓦松迴歸、負二項迴歸及線性迴歸建立關聯性模型，分析影響涉入事故與否、受傷程度、事故件數、事故型態、涉入事故車種、駕駛風險及駕駛行為的因素，並以逐步迴歸排除不顯著的變數。最後再以ROC曲線選擇鑑別力較高的二元分類模型。研究結果發現：年齡、教育程度、三年內違規經驗、駕駛適性診斷測驗的壓力緊張總分、危險感知測驗的市區路邊車輛開車門和山區路人行走中間和山區右岔路車輛駛出、行車金頭腦測驗科技新知分數和維護保養分數均為顯著影響的因素。其中，駕駛人三年內的違規經驗越多，風險越高（模型預測結果落在高風險組）。本研究亦發現某些顯著變數的系數正負號與常理相悖（如：安全感知總分越高的駕駛人越易涉入汽車碰汽車的事故）。
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.