便捷之交通工具-機動車輛，係使駕駛者駕駛頻率較高的工具。不僅如此，交通環境間影響機會更甚。謹此議題，道路駕駛者難免會面臨道路交通事故考驗。能合理預見在道路交通事故之車輛或與人之碰撞，導致傷亡的案例。交通意外肇事構成各駕駛人與社會的成本。本研究數據來源係交通運輸部公開資料庫，包括事故撞擊車輛數、車輛種類、都市型、觀光地域、路況環境、時間以及人等因素，對臺灣機車的交通肇事率的影響與否。本研究採用多元逐步迴歸方法，測試臺灣機車的交通肇事率及其外部因素，並探究相關外部因素對交通肇事率的影響與五年跨年比較。五年期間的實證迴歸模式調整判定係數平均為77%。(1) 機車撞擊車數對台灣機車的交通事故率具有顯著的正面影響，(2) 在機車車種因素且與自用大貨車相較下，2017至2021年民營公車-大客車對機車的交通肇事率具有顯著的負面影響，2017及2021年計程車-小客車對機車的交通肇事率具有顯著的負向影響。在車種因素中，遊覽車-大客車、營業用-全聯結車對2017年機車的交通肇事率有顯著的正面影響。在車種因素且與自用小貨車相較下，營業用-小貨車(含客貨兩用車)對2017至2021年機車的交通肇事率具有顯著的正面影響。在車種因素中，租賃車-小客車對2017年至2021年機車的交通肇事率具有顯著的正面影響。(3) 機車人的因素，與其他人相較下，乘客的行為對2017至2021年機車的交通肇事率具有顯著的負面影響，而行人的行為對2017至2021年機車的交通肇事率具有顯著的正面影響。(4)機車於都會區，2017至2021年新北市對機車的交通肇事率具有顯著的負面影響。2018至2021年臺北市相較對機車的交通肇事率具有顯著的負面影響，但是在2017年臺北市相較對機車的交通肇事率具有顯著的正面影響。2017年至2021年桃園市對機車的交通肇事率具有顯著的正面影響。2017年和2018年臺中市對機車的交通肇事率具有顯著的正面影響，但對2019年至2021年的機車交通肇事率具有顯著的負面影響。2017年台南市對機車的交通肇事率具有顯著的負面影響，但在2018年至2021年台南市對機車的交通肇事率具有顯著的正面影響。2017和2018年高雄市對機車的交通肇事率具有顯著的正面影響。不過，2019年至 2021 年高雄市對機車的交通肇事率具有顯著的負面影響。(5) 機車於路況環境因素且與其他路況相較下，在2017、2019和2020年廣場的路況對機車的交通肇事率具有顯著的負面影響。在2019到2021年圓環的路況對機車的交通肇事率具有顯著負面影響。2017至2021年交叉路口的路況對機車的交通肇事率皆具有顯著負面影響。(6) 機車於旅遊區，與其他區相較下，2017至2021 年非觀光區對機車的交通肇事率皆具有顯著的正面影響。然而，從2017到2021年國家公園區域及國家風景區域皆對機車的交通肇事率具有顯著的負面影響。(7) 機車於時間因素，在2021年的時間月初及月中，皆對機車的交通肇事率具有顯著的負面影響。然而與週末相較下，2018年星期一及星期二對機車的交通肇事率皆具有顯著的正面影響，但在2020年的星期一則對機車的交通肇事率呈現顯著的負面影響。2017年星期三對機車的交通肇事率具有顯著的正面影響。但在2018至2021年星期六對機車的交通肇事率具有顯著的負面影響。(8) 機車於特定假日或假期，2017、2019、2020和2021年國定假日或假期對機車的交通肇事率具有顯著的負面影響。(9)基於季節且與冬天相較下，2021年春天對機車的交通肇事率具有顯著的負面影響。而2021年秋天對機車的交通肇事率具有顯著的負面影響，但2017和2018年秋天對機車的交通肇事率具有顯著的正面影響。

Vehicles are convenient means of transportation, so the frequency of driving is higher and its environment affect each other. Based on this issue, as drivers will inevitably face challenges while on the road traffic incident. it is reasonably foreseeable to believe the potential for collision with vehicles or people in road traffic incident to appear cases of bodily injury and death oriented. Such traffic accident represents a costs to parties and to society. In this study, the data for this research were obtained from the ministry of transportation public database. It consists of some factors included the the number of vehicles hit, types of vehicle, human, metropolis, road conditions, specific tourist area and time on the traffic accident rate of motorcycle in Taiwan. We use stepwise regression method to investigate the relationship between the Taiwan’s traffic accident rate of motorcycle and its external factors, then their effects and comparison with 5 years of the traffic accident rate of related factors were explored. Results of adjusted coefficient of determination under the empirical model is 76 percent on average during 5 years. The empirical findings are as follows: (1) the number of vehicles hit has significant positive impact on traffic accident rate of motorcycle from 2017 and 2021, (2) based on vehicle types and compared to those large truck for personal use, the private bus-bus has significant negative impact on traffic accident rate of motorcycle from 2017 to 2021. The taxi-sedans has significant negative impact on traffic accident rate of motorcycle from 2017 and 2021. However, vehicle type and compared to these large truck for personal use, the tourist coach and business use - trailers has significant positive impact on traffic accident rate of motorcycle in 2017. vehicle type and compared to these minivans for personal use, the business use - small truck has significant positive impact on traffic accident rate of motorcycle from 2017 to 2021. Based on factors in vehicle type, the rental car - passenger car has significant positive impact on traffic accident rate of motorcycle from 2017 to 2021. (3) based on human factor and compared to other people, the behavior of pedestrian has significant positive impact on traffic accident rate of motorcycle from 2017 to 2021. The behavior of passengers has significant negative impact on traffic accident rate of motorcycle from 2017 to 2021. (4) based on metropolis factor and compared to non- metropolis, New Taipei city has significant negative impact on traffic accident rate of motorcycle from 2017 to 2021. Taipei city has significant negative impact on traffic accident rate of motorcycle from 2018 to 2021. New Taoyuan city has significant positive impact on traffic accident rate of motorcycle from 2017 to 2021. Taichung City has significant positive impact on traffic accident rate of motorcycle in 2017 and 2018, but which has significant negative impact on traffic accident rate of motorcycle from 2019 to 2021. Tainan City has significant negative impact on traffic accident rate of motorcycle in 2017, but which has significant positive impact on traffic accident rate of motorcycle from 2018 to 2021. Kaohsiung City has significant positive impact on traffic accident rate of motorcycle in 2017 and 2018, but which has negative significant impact on traffic accident rate of motorcycle from 2019 to 2021. (5) based on road conditions fortor and compared to others, the road conditions in the square has negative significant impact on traffic accident rate of motorcycle from 2017 and 2020 except 2018. The road conditions in the ring has negative significant impact on traffic accident rate of motorcycle in 2019 and 2021. The road conditions at intersections has negative significant impact on traffic accident rate of motorcycle from 2017 to 2021. (6)based on specific tourist area and compared to others, tourist area
tourist area has significant positive impact on traffic accident rate of Motorcycle from 2017 to 2021. But National Park and National Scenic Area has significant negative impact on traffic accident rate of Motorcycle in from 2017 to 2021. (7) based on time factors and compared to the weekend, Both the beginning and the middle of the month in 2021 have a significant negative impact on motorcycle traffic accident rates. However, compared with weekends, both Mondays and Tuesdays in 2018 had a significant positive impact on motorcycle crashes, but in 2020 Mondays had a significant negative impact on motorcycle crashes. Wednesday 2017 had a significant positive impact on motorcycle traffic crashes. But Saturday has a significant negative impact on motorcycle traffic crashes from 2018 to 2021 (8) based on special holiday, this period of national holiday has significant negative impact on traffic accident rate of Motorcycle from 2017 and 2021 except 2018. (9) based on season and compared to the winter, the season of spring has significant negative impact on traffic accident rate of private sedans in 2021. The season of autumn has significant positive impact on traffic accident rate of motorcycle in 2017 and 2018, but which has significant negative impact on traffic accident rate of motorcycle in 2021.

目錄
摘要 i
英文摘要 iii
目錄 vi
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究架構及流程 3
第二章 文獻回顧 5
2.1 機車交通肇事率 5
2.2 機車肇事率之內部因素之相關研究 5
2.3 影響機車肇事率之外在因素相關研究 7
2.3.1 機車撞擊人數 7
2.3.2 機車之車種因素 7
2.3.3 機車之都市地域因素 7
2.3.4 機車之觀光地域因素 8
2.3.5 機車之路況環境因素 8
2.3.6 機車之時間因素 9
2.3.7 機車與人之因素 9
2.3.8 機車之其他外部因素 9
3. 研究統計假設 10
第三章 機車交通肇事率之概述 13
3.1 機車交通肇事率之現況 13
3.1.1機車交通肇事率定義與現況 13
3.1.2 機車交通肇事率之概況 13
3.2 臺灣機車肇事率的趨勢 15
3.2.1 機車車禍事故肇事率 15
3.2.2 機車車禍事故死亡率 15
3.2.3 機車車禍事故受傷率 16
3.2.4 機車交通肇事因素之概述 17
3.2.4.1 機車車種因素 17
3.2.4.2 機車都市地域因素 18
3.2.4.3 機車觀光地域因素 19
3.2.4.4 機車路況環境因素 19
3.2.4.5 機車時間因素 20
3.2.4.6 機車人的因素 22
第四章 研究方法 24
4.1 多元逐步迴歸實證模式 24
第五章 實證結果與分析 28
5.1 樣本資料說明 28
5.2 實證樣本變數說明 29
5.2.1 研究變數 30
5.3 敘述統計分析 33
5.4 多元逐步迴歸實證結果與分析 40
5.5 外部因素對機車肇事率的跨年分析表 54
第六章 結論與建議 65
6.1 結論 65
6.2 建議 66
參考文獻 68
附錄一 71
附錄二 77
附錄三 129
附錄四 134
附錄五 137

表目錄
表1.1 臺灣車輛總肇事件數、機車肇事件數統計比較表 2
表5.1 本研究模式之變數名稱、定義及相關說明表 30
表5.2 描述性統計資料表 33
表5.3 機車非連續變數或類別變數 -- 次數表 37
表5.4 多元逐步迴歸法模式摘要表 40
表5.5 各年多元逐步迴歸法迴歸結果表 48
表5.6 外部因素對機車肇事率之跨年分析表 56
表5.7 多元逐步迴歸法變異數分析摘要表 59
表5.8 多元逐步迴歸的共線性診斷表 60

圖目錄
圖1.1 本研究架構與流程圖 4
圖3.1a臺灣每萬輛機動車輛肇事率趨勢圖 14
圖3.1b臺灣2020年機動車輛肇事率佔比 14
圖3.2 臺灣機車肇事率趨勢比較長條圖 15
圖3.3 臺灣每萬輛機車之死亡率趨勢折線圖 16
圖3.4 臺灣每萬輛機車之受傷率趨勢折線圖 16
圖3.5 臺灣警政署各類車種肇事統計圖 17
圖3.6 臺灣六都肇事件數統計圖 18
圖3.7 臺灣路況事故件數佔比圖 20
圖3.8 臺灣逐月肇事件數統計趨勢圖 22
圖3.9 臺灣行人及乘客交通事故死亡人數趨勢圖 22

一、中文部分
1. 賴麗華 (編譯) (2004)。風險管理國際觀－並論保險產業定位 (初版)，美國麥格羅．希爾國際出版公司。(Harold D. Skipper, Jr., 1997)
2. 蕭文龍 (2007)，多變量分析最佳入門實用書，碁峯資訊公司，臺北市。

二、網路部分
1. 中華民國交通部交通統計名詞定義—公路工程統計。https://www.motc.gov.tw
2. 內政部統計通報2021年第40週統計表。https://www.moi.gov.tw/cl.aspx?n=13331
3. 交通部統計查詢網。https://stat.motc.gov.tw/mocdb
4. 警政署交通組統計室。https://www.npa.gov.tw/ch/app/data
5. 國家教育研究院。https://terms.naer.edu.tw/detail/1310684/
6. 臺中市政府警察局交通警察大隊。https://www.police.taichung.gov.tw/traffic/index.jsp
7. 臺灣博碩士論文知識加值系統。https://hdl.handle.net/11296/wzn4cg

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11. Ospina-Mateus, H., Quintana Jimenez, L. A, Lopez-Valdes, F. J., & Salas-Navarro, K. (2019). Bibliometric analysis in motorcycle accident research: a global overview。Scientometrics, 121, 793-815. http://dx.doi.org/10.1007/s11192-019-03234-5.
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