臺灣博碩士論文加值系統

交通違規舉發常被預期可以防止駕駛人未來發生交通事故的手段之一，故本研究旨在找出交通違規舉發與交通事故的發生之關聯性。結合2011-2017年警政署事故資料庫、公路總局第三代公路監理資訊系統駕駛人主檔、違規主檔、車籍異動檔等，以及衛生福利部死因資料庫，進行資料庫清洗和串聯。將交通違規種類分為31種，並根據違規和事故資料之車籍車種欄位，將駕駛人分為汽車駕駛人和機車駕駛人，以進行分析。先以敘述統計分析汽機車駕駛人曾經被舉發違規下，其駕駛人和未來發生事故數的比率關係，再以關聯規則，分析這些被舉發違規的汽機車駕駛人，和未來發生事故的關聯。為了再進一步了解曾經被舉發違規和其不同駕照狀態下的汽機車駕駛人，從第一次違規舉發到第一次發生事故，其發生事故率和時間的關係，先繪製Kaplan-Meier (K-M)存活曲線觀察不同駕照狀態和曾經被舉發違規的汽機車駕駛人之發生事故的存活率，也就是不發生事故的機率。再以Cox等比例風險模式分析影響駕駛人單一事件時間變數的相關變數，包括曾經被舉發的違規種類，以及汽機車駕駛人之駕照狀態。而Cox等比例風險模式卻只能分析駕駛人單一事件的存活時間，但駕駛人在研究期間內，可能不僅僅只有一件交通事故的發生，可能還會伴隨著多件交通事故，使得一個駕駛人會有多個事件的存活時間，無法以Cox等比例風險模式分析。因此，引入存活理論常使用的復發事件邊際模型，包括AG模型和PWP模型，以對擁有多個事件的駕駛人，進行曾經被舉發的違規種類和汽機車駕駛人駕照狀態之變數分析。
Kaplan-Meier存活曲線結果指出，在不同駕照狀態之汽機車駕駛人中，「僅有機車駕照之汽車駕駛人」發生事故存活曲線下降最快，且最終存活率僅有0.845。「有機車駕照之機車駕駛人」和「有汽車駕照之汽車駕駛人」中，機車駕駛人的發生事故存活曲線下降較快，但機車駕駛人之最終存活率為0.849，汽車駕駛人之最終存活率為0.848。在存活理論復發事件PWP模型的結果下，「僅有機車駕照之汽車駕駛人」未來發生事故的時間危險率為1.48。而在曾經被舉發「車輛設備和規格違規」、「抗拒稽查或肇逃」的機車駕駛人，相較於無違反此類違規的人，未來發生事故的時間危險率上升至1.10和1.23。曾經被舉發「酒駕和藥駕」的汽機車駕駛人，其舉發違規對於駕駛人未來發生事故的時間危險率分別下降至0.62和0.50。被舉發違規的駕駛人，未來發生事故的關聯和時間危險率，會因不同的違規種類之舉發，而有所不同。

Traffic enforcement is usually expected to add the effectiveness of accident prevention. Therefore, the relationship between traffic violation records and traffic accident needs to be investigated. In this study, the combination of Taiwan’s national traffic accident database, national traffic violation record database, and driver and car registration database is used, and drivers are divided into car drivers and motorcyclists for analysis. This study first analyzes the accident involvement rate of car drivers and motorcyclists with past violation records by the descriptive statistic analysis. Then, the relationship between accident and those drivers is discovered by association rule. Furthermore, it is desired to understand the change of survival time between the first violation records to the first accident of drivers with different driver’s licenses and past violation records, the Kaplan-Meier curve is applied. Then it uses Cox proportion hazard function for further discussion of the relating factors, such as the past violation records and driver’s licenses. However, the Cox proportion hazard function is only used to analyze the single event in each driver. The driver may involve multiple accidents in the research period. The survival time in multiple accidents cannot be analyzed in the Cox proportion hazard function. Therefore, the marginal model of the recurrent events in the survival theory is applied in the study, including the AG model and the PWP model. The survival time of multiple accidents in each driver can be modeled.
Based on the result of the Kaplan-Meier curve for analyzing the different driver’s licenses, the survival curve of car drivers only with motor driver’s license decline quickly than others, and the last survival rate is 0.845. The last survival rate of car drivers with car driver’s license and motorcyclists with motor driver’s license are 0.848 and 0.849 respectively. Based on the further result of the PWP model for analyzing the different driver’s licenses, the hazard ratio of car drivers only with motor driver’s license is increasing to 1.48. For analyzing the past violation records, the hazard ratio of motorcyclists cited by “Improper vehicle equipment” and “Resisting inspection or Hit-and-Run” is increasing to 1.10 and 1.23 respectively. However, the hazard ratio of car drivers and motorcyclists cited by “Drunk and drug driving” is declined to 0.62 and 0.50 respectively. Besides, car drivers and motorcyclists with other violation records can be shown in the different hazard ratio.

口試委員會審定書 I
誌謝 II
摘要 III
ABSTRACT IV
目錄 V
圖目錄 VIII
表目錄 X

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究問題 1
1.3 研究目的 2
1.4 研究流程 2

第二章 文獻回顧 3
2.1 違規分類 3
2.2 事故嚴重度之分級和定義 5
2.3 評估變數對交通違規及事故之影響 6
2.3.1 交通事故嚴重度分析 6
2.3.2 總體事故頻率分析 10
2.3.3 國內相關研究 13
2.3.4 小結 13
2.4 存活理論 13
2.5 小結 24

第三章 研究方法 26
3.1 關聯規則 26
3.2 存活理論函數定義 28
3.3 資料設限形式 29
3.4 KAPLAN-MEIER存活曲線 30
3.5 COX等比例風險模式 32
3.6 復發事件邊際模型 34

第四章 資料蒐集與處理 37
4.1 違規和事故資料庫之處理 37
4.2 違規種類整理和分類 41
4.3 車籍及駕籍整理和分類 45
4.4 違規、事故及駕籍資料整理和串聯 47

第五章 分析結果 54
5.1 分析流程和模式求解工具 54
5.2 曾經違規記錄和未來發生事故之資料統計 55
5.3 違規到事故發生之KAPLAN-MEIER存活曲線 68
5.3.1 不同駕照狀態之汽機車駕駛人 68
5.3.2 不同違規種類次數之汽機車駕駛人 69
5.4 存活模型參數校估結果 77
5.4.1 單一事件之Cox模型 77
5.4.2 復發事件邊際模型 81
5.4.3 模型選擇 90
5.5 結果比較和討論 91

第六章 結論與建議 97
6.1 結論 97
6.2 後續建議 102

參考文獻 103

附錄一 違規種類次數和交通事故之關聯圖 108
附錄二 違規種類次數和交通事故發生時間之存活曲線 121
違規汽車駕駛人第一次違規到第一次發生事故之存活曲線 121
違規機車駕駛人第一次違規到第一次發生事故之存活曲線 134

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