應用決策樹法於摩托車事故之研究

Abstract

隨著經濟的發展交通運輸工具越來越普及與便利造成每年的交通事故件數不斷的攀升，以台北市為例每年發生至少13000起交通事故。機車事故是主要發生車禍的車種。交通事故的發生是不同時間、環境等因素所構成，因此每個發生交通事故嚴重性皆有所不同，難以去分析。為確實掌握交通事故發生原因及肇事防治工作，本研究取得交通肇事資料進行分析，以降低肇事案件發生，維護駕駛及路人安全。道路交通事故調查表登載內容包括環境因子、交通設施及當事人資料等35項，經過其資料前置處理包含資料過濾、值域正確性判斷、遺漏值插補處理，建立本研究肇事分析資料庫後運用決策樹對其做資料探勘。 本研究採用台北市交通局民國97年至102年共六年的台北市機車交通事故資料從機車乘車者的受傷程度做駕駛行為、道路環境條件等因果關係並運用資料探勘技術中的決策樹CHAID演算法來驗證肇事分析期望找出台北市交通事故的主要問題。歸納出北市交通事故相關因子提供對北市具體改善策略與建議並降低未來事故發生率。

With the development of the economy and improvement of people’s living, Transportation has become more popularity and convenient. In recent years, the traffic accident rate has increased sharply. In Taipei, there are more than 13,000 traffic accidents every year, with the motor vehicle accident being one of the highest traffic proportion among all the car types. There are different factors of traffic accidents, including time, environment, weather…etc. Each traffic accident may cause different injure level. According to the traffic data from the Department of Transportation in Taipei City, there are 35 categories from traffic record including environmental factors, transport facilities and personal information. And it’s hard to analysize all the factors simultaneously. To better understand the traffic factors and prevent citizens from traffic accidents. This study will preprocessing the traffic accident data from 2008 to 2013 and use decision tree (CHAID) to analyize the traffic data, figure out the main factors of motor vehicle accidents in Taipei capital, and provide valuable information for the Department of Transportation in Taipei City to support their decision-making, transportation planning, reduce the traffic accidents.