運用資料採礦技術探討數位式行車紀錄器於公路客運駕駛員異常操作行為管理之研究

淡江大學機構典藏 > 商管學院 > 運輸管理學系暨研究所 > 學位論文 > Item 987654321/33801

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題名:	運用資料採礦技術探討數位式行車紀錄器於公路客運駕駛員異常操作行為管理之研究
其他題名:	A study of digital tachograph in the abnormal driving behaviors management of bus drivers using data mining techniques
作者:	高啟涵;Kao, Chi-han
貢獻者:	淡江大學運輸管理學系碩士班羅孝賢;Luo, Shiaw-shyan
關鍵詞:	資料採礦;多元迴歸分析;群集分析;判別分析;駕駛員管理;Data Mining;Regression Analysis;Cluster Analysis;Discriminant Analysis;Driver Management
日期:	2006
上傳時間:	2010-01-11 04:33:32 (UTC+8)
摘要:	對於公路運輸服務業而言，異常駕駛行為除直接導致潛在行車肇事或交通違規，提高業者經營風險，亦會降低行車服務品質，造成車輛無謂耗損徒增保養維修費用。如何針對駕駛員異常操作情形作出有效管理以及增進行車安全，是客運業者亟待解決的問題。隨著科技進步，智慧型運輸系統（Intelligent Transportation System, ITS）發展日益蓬勃，國內公路客運業者已漸由傳統「機械式行車紀錄器」改用數位式行車紀錄器。惟數位式行車紀錄器蒐集項目眾多與資料量相當龐大，如能夠有效分析與利用，對於公路客運業者行車安全、油耗與保修費用以及駕駛員管理將有所助益。本研究主要以公路客運為研究對象，蒐集國內某客運公司61部客運車輛數位式行車紀錄器資料、210名駕駛員資料與肇事、交通違規、油耗與保養維修資料進行分析探討，資料蒐集時間為民國93年6月至民國94年12月。資料採礦首要步驟為定義與確認駕駛異常操作行為項目與門檻值，以萃取資料庫中有意義的資訊。運用多元迴歸分析與群集分析建立資料採礦模式，前者構建駕駛員異常行為關係模式，以釐清駕駛員異常行為對於行車安全、油耗與保修費用之影響關係；後者將相同駕駛特性駕駛員進行分群，使能與後續駕駛員管理配合運用。研究結果發現，影響肇事次數為急減速與超速，影響交通違規次數為超速與電磁煞車操作異常；影響油耗費用為急加速、怠速過久以及引擎轉速異常，影響保養維修費用則為急減速、急加速、電磁煞車操作異常以及引擎轉速異常。群集分析將210名駕駛員分成三群，分別命名為一般等第199人，稍差等第9人、極差等第2人，分群結果符合一般現況，並構建判別模式可直接判別駕駛員所屬之群集等第。整合上述資料採礦結果提出可落實於駕駛員管理層面整合流程，與駕駛員管理獎懲案例與方法。最後評估以再教育訓練或相關管理方式提昇駕駛員素質水準之效益，結果得到一個月可以減少13次肇事、2次交通違規，以及公司可節省745,480元額外油耗與保養維修費用，可利用節省費用提撥一定比例供再教育訓練用與新進駕駛員訓練，或增購數位式行車紀錄器進行車輛進行全面裝設，以充分掌握所有駕駛員駕駛行為資料，始能搭配相關管理措施以進行有效管理。 For bus carriers, the abnormal driving behaviors will not only cause a higher risk of accident and traffic offence, but also deteriorate the vehicle worn-outs, which will cause bus service broken-down. Consequently, how to monitor and manage abnormal driving behaviors effectively and efficiently is an important issue to bus operators. With the progress of science and technology, Intelligent Transportation System is developed flourishingly. Today, many bus carriers have used digital tachographs to record the bus driving details. Particularly through data mining, the extraction of hidden predictive information from large databases will help us to find out and identify the relationship among abnormal driving behavior and driving safety, fuel consumption, and maintenance cost. This study collected data from digital tachograph database, which include 61 buses, 210 drivers'' data. In the meanwhile, the vehicles related accident and traffic offence records, fuel consumption and maintenance cost data were also collected from June 1, 2004 to December 31, 2005. The first step of data mining is to confirm and define the variables and related threshold values of abnormal driving behaviors in order to extract the meaningful information. The data mining techniques were used in this study, such as multiple regression analysis and cluster analysis. Multiple regression models were developed to establish the empirical relationship among abnormal driving behaviors and driving safety, fuel consumption, and maintenance cost. The cluster analysis was applied to categorize the sample of drivers which have similar driving characteristics. The discriminate analysis was used to determine the driver''s level directly. The findings of multiple regression models indicated that the emergent deceleration and speeding variables are the key determinants of the frequency of accident; speeding and abnormal operation of electromagnetic braking variables are the key determinants of the frequency of traffic offence; emergent deceleration and acceleration, long idle time of engine operation, abnormal operation of electromagnetic braking and abnormal engine rotation variables are the key determinants for fuel consumption and maintenance cost. The cluster analysis has classified the 210 drivers into 3 category levels: fair, bad and very bad. In this case, there are 199 drivers in fair level, 9 drivers in bad level, and 2 drivers in very bad level. According to the data mining results, this study proposed an integrated driver management solution. After evaluation, it is shown that applying driver management strategies such as re-education, rewards and punishments, on a monthly basis, the frequency of accident can be reduced by 13 times, frequency of traffic offence by 2 times and a cost of NT$745,480 for extra fuel consumption and maintenance cost are saved. The above savings could be used alternately for employees'' re-education and training, or to equip digital tachographs on the bus fleets.
顯示於類別:	[運輸管理學系暨研究所] 學位論文

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