本研究主要目的為針對雪山隧道路段車輛偵測器資料發生遺漏時進行即時填補。填補方法有空間填補與時間填補兩大種類，因為在填補過程中不考慮本身歷史資料與鄰近偵測器歷史資料投入填補，所以是採用空間填補方式。當路段上有數組偵測器資料發生遺漏時，必須利用剩餘完整偵測器來當作投入填補的資源。透過鏈結串列的應用，將遺漏偵測器與完整偵測器區隔開，分別建立出資料遺漏偵測器串列與資料完整偵測器串列。最後對遺漏偵測器依序進行填補，其填補組合是利用完整偵測器依照投入組數的不同透過組合方法將所有填補組合計算得出。最後從這些填補組合中搜尋出各遺漏偵測器的最佳填補組合。 結果顯示，依照遺漏偵測器數量不同，其填補組合的數量也不同。但因為在4組以上偵測器投入填補的填補組合數量過多又填補績效以隨機方式假設，故4組以上的局部最佳填補組合的填補績效值皆為5%居多。接著再從這些局部最佳填補組合選擇最多組偵測器投入作為遺漏偵測器的最佳填補組合。To search for the optimal imputation of Vehicle Detector, this paper using the Data Structure-Linked Lists to search the optimal solution of imputation of Vehicle Detector. We have to establish three the essential VD Lists and the numbers of data imputation Lists. Using the Lists to find the optimal solution of missing values VD.First, use the 35 VD to establish the original VD Lists and copy the data of the original VD Lists to establish another List, it’s call data complete lists. Then we input the VD of missing values to delete VD of data complete lists, at the same time, use the VD of missing values to establish missing data lists. And then using combination of statistics of data complete lists to find the imputation mode, MAPE use the random to decide. Use the imputation mode to establish the numbers of data imputation Lists. Finally , we can search the local optimal solution from the data imputation Lists, and find the optimal solution from those local optimal solution.The result showed that every VD of missing value the local optimal solutions MAPE are 5%, because the imputation mode is too much and the MAPE is random to decide. But we have to find the optimal solution of missing value VD, so according to Literature, we choose the most VD imputation Data is optimal solution.
