臺灣博碩士論文加值系統

依據內政部警政署的統計，機動車輛所造成交通事故一直是這幾年台灣死因的前幾名，所以顯而昜見的車禍肇事意外已經嚴重影嚮到國民的生命財產之安全與保障。因此如何降低交通事故發生率，讓人民財產損失減少，生命獲得保障儼然是目前最重要的課題之一。

隨著近年來資料探勘技術的發展，其應用在各個學門都有良好的成效，因此本研究將針對交通事故的現場環境，利用資料探勘技術來挖掘當中隱含的資訊，希望能提供給相關部門，對於交通工程、道路環境設計或建造時一個參考，以期望能使得肇事發生率能獲得有效的改善，降低交通事故對於人民生命財產的威脅。

基於以上因素，本研究利用關聯規則方式來找尋造成車禍之環境成因，以及運用決策樹分析能提供明顯分類特徵，並且能夠支援數值型及類別型資料之特色，針對交通事故環境及車禍間關係來發展預測模型，並以88及89年度車禍記錄作為訓練資料，90年度車禍記錄作為測試資料，其所得結果大部份能有6成以上預測準確度。此預測模型圖能提供作為檢示道路危險程度之依據，也能作為開發交通事故專家系統之基礎。

Traffic accidents are one of the critical reasons to cause deaths in Taiwan based upon the Interior Department analysis. One of the most important tasks for our government is to reduce the number of traffic accidents as well as to protect the lives of people in Taiwan. As data mining techniques have been well developed and widely and successfully applied in many areas, this study uses data mining techniques to discover the hidden information in the raw data to provide useful information as a reference by improving the traffic environment.
According to the above discussions, this study finds out the reasons that result in traffic accidents by association rules. Moreover, decision trees are applied to provide a clear indication of which fields are most important for prediction or classification as well as to handle both continuous and categorized variables. The relation between the traffic environments and traffic accidents can be used to develop the prediction model that leads more than 60% of accuracy in prediction. Finally, the road hazardous degrees can be defined and the basic expert system for traffic accident analysis can be developed based upon the prediction model.

中文摘要..........................................i
英文摘要..........................................ii
誌謝..........................................iii
表目錄..........................................v
圖目錄..........................................vii
一、緒論..........................................1
二、文獻探討..........................................3
2.1 車禍肇事之特性及相關研究..........................................3
2.2 資料探勘定義與相關技術..........................................4
2.3 關聯規則..........................................5
2.4 Apriori演算法..........................................6
2.5 決策樹..........................................7
三、研究流程與設計..........................................11
3.1 資料來源與問題釐清..........................................11
3.2 研究方法與流程..........................................12
四、研究結果與分析..........................................17
4.1 關聯規則分析..........................................17
4.2 預測模型分析..........................................24
五、結論與建議..........................................34
參考文獻..........................................36
附錄一 決策樹模型圖..........................................39
附錄二 決策樹分析預測率之區間估計..........................................51

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