臺灣博碩士論文加值系統

職業駕駛，包括外送員、計程車司機、公車司機和貨車司機等，因長時間在道路上行駛，面臨較高的風險。這 些職業駕駛常需要在規定時間內抵達目的地，因此容易出現疲勞駕駛、超速和超載的情況。在這種高壓的工作環境下，駕駛更容易做出危險行為，進一步增加行車風險。
台灣於民國111年共有3064位用路人因交通事故死亡，發生故事數是日本的六倍、英國的五倍，其中394位為行人。由於近年來交通議題逐漸受到民眾重視，台灣的交通亂象也曾被外媒報導，形容台灣是「行人地獄」。
本論文探討了台灣六都的交通事故現象，收集了民國110年至111年台北市職業駕駛的交通事故資料，並使用Tableau作為視覺化分析工具，呈現事故的空間分布、事發時間等特徵。我們深入分析了事故發生的主要原因，包括道路設施、駕駛人是否酒駕、天氣狀況等多個因素。
根據分析結果，本論文提出了一系列交通安全改善策略，包括改進事故多發路段的道路設施、強化駕駛宣導等方面的建議。這些策略不僅可以降低事故風險，也有助於提升整體交通安全水平。

Professional drivers, including delivery workers, taxi drivers, bus drivers, and truck drivers, face higher risks due to the long hours spent on the road. These professional drivers often need to reach their destinations within a specified time frame, which increases the likelihood of fatigue, speeding, and overloading. In such a high-pressure work environment, drivers are more prone to engage in dangerous behaviors, further elevating the risk of traffic accidents. In Taiwan, a total of 3,064 road users died in traffic accidents in 2022, with the number of accidents being six times that of Japan and five times that of the United Kingdom. Among these fatalities, 394 were pedestrians. As traffic issues have gained increasing attention from the public in recent years, Taiwan's traffic chaos has even been reported by foreign media, describing Taiwan as a "pedestrian hell."
This paper explores the traffic accident phenomena in Taiwan's six major cities, collecting traffic accident data involving professional drivers in Taipei City from 2021 to 2022. Tableau was used as a visualization tool to present the spatial distribution, timing, and other characteristics of the accidents. We conducted an in-depth analysis of the main causes of these accidents, including factors such as road facilities, whether the driver was under the influence of alcohol, and weather conditions. Based on the analysis results, this paper proposes a series of traffic safety improvement strategies, including recommendations for improving road facilities in accident-prone areas and strengthening driver education. These strategies not only aim to reduce the risk of accidents but also contribute to enhancing overall traffic safety.

摘 要...i
Abstract...ii
誌 謝...iv
目 錄...v
表目錄...vii
圖目錄...viii
第一章 緒論...1
1.1研究背景...1
1.2研究目的...2
1.3研究對象...2
1.4研究方法...2
第二章 文獻探討...4
2.1開放資料...4
2.2視覺化分析...5
2.3大數據資料...8
2.4交通事故相關研究...9
2.5職業駕駛相關研究...10
第三章 研究方法...15
3.1Tableau軟體介紹...15
3.1.1直觀的拖放式界面...15
3.1.2強大的數據連接和整合...16
3.1.3即時數據分析...16
3.1.4多樣的視覺化選擇...17
3.1.5交互式儀表板...17
3.1.6預測和趨勢分析...18
3.1.7數據共享和協作...18
3.2資料來源...18
3.3交通工具構面...20
3.4時間構面...22
3.5年齡構面...22
3.6駕照構面...22
第四章 研究結果...24
4.1統計分析結果...24
4.1.1次數分配...24
4.1.2車種與各變數的關係...31
4.1.3車種與肇事主因與各變數之關聯...36
第五章 結論與建議...43
5.1結論...43
5.2建議...43
5.3務實面建議...43
5.3.1駕駛培訓及教育...44
5.3.2技術設備的提升...44
5.3.3完善的規章制度...45
5.3.4改善道路設施和環境...45
5.4未來研究方向...46
參考文獻...48
附錄A...50
Extended Abstract...53

1.余姿萱（2022）。應用洪水模擬大數據建立淹水雨量警戒值之分析。﹝碩士論文。國立臺北科技大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/544fyn。
2.蔡承晏（2022）。交通大數據分析以公車動態資料為例。﹝碩士論文。國立臺灣師範大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/mwf4c6。
3.魏駿宇（2023）。工地危害辨識資料視覺化之探討。﹝碩士論文。國立陽明交通大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/85278r。
4.賴泱州（2015）。地方政府機關開放資料分享之影響因素探討─以臺中市政府為例。﹝碩士論文。國立臺灣大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/avjgp7。
5.Williamson, A., Friswell, R., Olivier, J., & Grzebieta, R. (2014). Are drivers aware of sleepiness and increasing crash risk while driving?. Accident; analysis and prevention, 70, 225–234. https://doi.org/10.1016/j.aap.2014.04.007
6.McCartt, A. T., Rohrbaugh, J. W., Hammer, M. C., & Fuller, S. Z. (2000). Factors associated with falling asleep at the wheel among long-distance truck drivers. Accident; analysis and prevention, 32(4), 493–504. https://doi.org/10.1016/s0001-4575(99)00067-6
7.Morrow, P. C., & Crum, M. R. (2004). Antecedents of fatigue, close calls, and crashes among commercial motor-vehicle drivers. Journal of safety research, 35(1), 59–69. https://doi.org/10.1016/j.jsr.2003.07.004
8.Nordbakke, S., & Sagberg, F. (2007). Sleepy at the wheel: Knowledge, symptoms and behaviour among car drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 10(1), 1-10.
9.Armstrong, K., Obst, P., Banks, T., & Smith, S. (2010). Managing driver fatigue: education or motivation? Road and Transport Research, 19(3), 14-20
10.Vanlaar, W., Simpson, H., Mayhew, D., & Robertson, R. (2008). Fatigued and drowsy driving: a survey of attitudes, opinions and behaviors. Journal of safety research, 39(3), 303–309. https://doi.org/10.1016/j.jsr.2007.12.007
11.Maynard, S., Filtness, A., Miller, K., & Pilkington-Cheney, F. (2021). Bus driver fatigue: A qualitative study of drivers in London. Applied ergonomics, 92, 103309. https://doi.org/10.1016/j.apergo.2020.103309
12.Regan, Michael & Lee, John & Victor, Trent. (2013). Driver distraction and inattention: Advances in research and countermeasures. 10.1201/9781315578156.
13.Xie, Peng & Li, Tianrui & Liu, Jia & Du, Shengdong & Yang, Xin & Zhang, Junbo. (2020). Urban flow prediction from spatiotemporal data using machine learning: A survey. Information Fusion. 59. 10.1016/j.inffus.2020.01.002.