類神經網路是一種模仿生物神經系統功能的資訊處理系統，可以應用在分類、預 測及最佳化等交通運輸問題上。網路的結構是由輸入層、輸出層與隱藏層所構成，由於缺乏 明確的準則，輸入層的變數對於期望的輸出結果可能助益不大，反而額外增加資料收集的困 難與網路運作的時間，如何選擇重要的輸入變數便是值得探討的重點。本研究以高雄港轉口 貨櫃運量預測為例，透過網路的構建與計算，利用貢獻圖的概念，得到影響性較顯著的因素 ，經由敏感度分析與預測作業，皆顯示不錯的結果。

　　　　　The inherent feature of artificial neural networks is an efficient information processing system. It has been successfully applied to various transportation problems of classification, prediction and optimization. The network structure is composed of input layer, output layer and hidden layer. Owing to lack of specific criteria, some input layer variables may be less relevant to the desired output. This would increase the difficulty of data collection and network operations. This study investigates the relationships between input and output elements using the contribution graph approach. Transit containers forecast in Kaohsiung port is employed for illustration. Significant inputs relationships are identified easily from the network. Based on the predicted volume and sensitivity analysis, the proposed approach is confirmed an efficient way to utilize the neural networks.