| 摘要: | 本研究主要目的為針對雪山隧道路段車輛偵測器之遺漏值，利用基因規劃法進行實證分析，以求得最佳填補函數。首先，進行單一屬性資料填補，採用上、下游累積偵測器資料並逐步向外對稱延伸累積，分別進行流量、速率、佔有率填補，分析填補績效與投入填補之車輛偵測器數量是否相關。接著以多屬性資料分別進行流量、速率、佔有率資料填補，以求得最佳之填補函數。之後，再將單一屬性與多屬性資料分別填補流量、速率、佔有率之績效進行排名，以求得進行填補時採用之優先順序。最後，將基因規劃法之填補績效與回饋式類神經網路進行績效比較。 結果顯示，利用基因規劃法之填補績效優於回饋式類神經網路之填補績效。綜合流量、速率與佔有率之填補績效，流量&速率填補排名第一，速率&流量&佔有率填補排名第二，而速率填補績效最差，排名第七。以填補流量績效而言，利用流量資料取上下游累積至第5組偵測器進行填補可獲得最佳填補績效，MAPE值為5.23%；以填補速率績效而言，利用流量&速率資料取上下游累積至第3組偵測器進行填補可獲得最佳填補績效，MAPE值為0.96%；以填補佔有率績效而言，利用佔有率資料，取上下游累積至第11組偵測器進行填補可獲得最佳填補績效，MAPE值為10.67%。 To search for the optimal imputation of Vehicle Detector, this paper, we carried out an empirical analysis for missing value of Hshehshan tunnel via Genetic Programming. We, at first, use signal attribute data impute missing value by accumulated nearest pairs of up- and downstream vehicle detectors, and analyze the relation between performance and number of vehicle detectors, whereupon, we imputed missing value by multi-attribute data. After testing data imputation, we ranked all types of imputation according to the performance. Finally, Recurrent Neural Network was selected to compare with Genetic Programming. The results showed that the performance of Genetic programming is better than Recurrent Neural Network. If we ranked all types of imputation according to conbined the three imputation performance, the rank as follows, flow&speed imputaiotn is 1st, speed&flow&occ imputation is 2st and speed imputation is the worst. For flow imputation, we use flow data, the accumulated nearest five pairs of detector up- and downstream could be input for the highest accuracy. For speed imputation, we use flow&speed data, the accumulated nearest three pairs of detector up- and downstream could be input for the highest accuracy. For occupancy imputation, we use occupancy data, the accumulated nearest eleven pairs of detector up- and downstream could be input for the highest accuracy. |
