本研究主要目的是利用偵測器時/空資料插補遺漏值，嘗試找出一最佳遺漏值插補組合與績效。首先在空間上採單一偵測器與累積偵測器輸入兩種模式，取上游、下游與上下游三種方式進行空間插補，藉此建立一個最佳的空間插補範圍。接著在時間上分為無待插補偵測器資料與含待插補偵測器資料兩種模式，將歷史資料以單一時間、累積時間與移動平均時間三種資料型態進行時間插補，最後經由插補績效評估得到一最佳時/空插補組合。本研究在分析插補績效前，先利用K-means把偵測器資料分群再以回饋式類神經網路針對流量、速率與占有率進行插補遺漏值的實證分析。結果發現：以流量插補，取上下游累積至第6組偵測器含自我資料的前20分鐘平均歷史資料，可以獲得最佳插補績效；以速率插補，取上下游累積至第7組偵測器含自我資料的前20分鐘平均歷史資料，可以獲得最佳插補績效；以占有率插補，取上下游累積至第6組偵測器含自我資料的前15分鐘平均歷史資料，可以獲得最佳插補績效。 The main purpose of this study is the use of detector temporal/spatial data to imput missing values, try to find a best combination and the performance of missing value imputation. At first, in spatial we adopted two modes：single detector datas and accumulation detector datas imputing miss values, and then get three spatial imputatiol ways:upstream, downstream,upstream and downstream detector, to establish an optimal range of spatial imputation. After that, in temporal we assign two modes:non-include interpolated detector data mode and include interpolated detector data mode, and we handled the historical data to a single time interval, accumulated time intervals and time interval moving average of three data types for temporal imputation, the final to get a performance evaluation by the best combination of temporal/spatial imputation.In this study, before analysis the imputation performance, we using K-means method to cluster detector datas, then the information(flow,speed and occupancy) using recurrent neural network to imput missing values and analysis. The results showed that:the flow of imputation, taking the upstream and downstream cumulative detectors to no.6 set with self-information the 20 minutes ago mean historical datas could get the best imputation performance; the speed of imputation, taking the upstream and downstream cumulative detectors to no.7 set with self-information the 20 minutes ago mean historical datas could get the best imputation performance; the occupancy of imputation, taking the upstream and downstream cumulative detectors to no.6 set with self-information the 15 minutes ago mean historical datas could get the best imputation performance.
