本研究主要針對高速公路上發生的交通事故，透過國內不同事故資料庫的整理，以彙整出相關之交通事故特性，並利用此特性找到對應事故延遲時間、等候車隊長度等事故衝擊特徵，藉此提供準確的交通資訊作為駕駛者、管理人員參考。
進行本研究需要得到過去發生在高速公路上的交通事故資料，本研究蒐集處理國道五號交通事故之相關單位，其資料來源包括「國道高速公路局拖救記錄服務表」、「警政單位道路交通事故調查表」，由於資料格式各有差異，因此本研究統一交通事故記錄的格式，以建立完整的國道五號事故資料庫。另外，本研究亦針對交通事故資料中各項屬性進行分析，利用國道高速公路局在國道五號全線佈置之車輛偵測器進行交通事故延遲時間及車隊等候長度之推估，最後根據本研究所建立之事故屬性組合建構類神經網路預測模式，提供準確之旅行者用路資訊。
根據類神經網路預測模式研究結果顯示，本模式具有準確之事故延遲時間、車隊等候長度之預測能力，對於用路人可有助於滿足了真實生活中之需求，對於高速公路管理單位在實際應用上具有穩定且準確之參考依據，藉此能夠發展更先進的交通控制系統。

Traffic accident occurrence on freeway is the main cause that affects travel time anticipated. Accurate travel time information will help travelers make better decisions in terms of departure time, route selection and even mode choice. This study aims to analyze the importance of accident features and evaluate the impacts of accident existence (queue length and recovery time) on freeway. The ultimate goal is to estimate accident duration and queue length when given relevant accident features according the accident database and vehicle detector database. All these information needs to be cross-checked in order to confirm the significant accident features and resulting impacts. Artificial neural network models are constructed with the most relevant factors identified in the above processes to provide predictive information of accident duration.

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