本研究採實地調查方式蒐集號誌轉換下， 可能影響駕駛者是否通過路口之行為 變數，然後，配合多變量因子分析及羅吉特模式進一步篩選出主要之變數以作為構建駕駛者 選擇行為模式之基礎。在模式建立方面，本研究利用類神經網路模式來探討駕駛者之不確定 行為，並由類神經網路構建之駕駛者行為模式進而推估猶豫區間，最後以所推估之猶豫區問 與實際猶豫區間作一比較，藉以確認本研究所得模式之有效性。結果顯示推估與實際之猶豫 區間兩者的一致性甚高，證實本研究構建之類神經模式的適用性。至於模式未來應用，可作 為號誌轉換時段設計之參考。

　　　　　To properly investigate a driver behavior at signalized intersections, an artificial neural network (ANN) architecture called back-propagation network (BFN) was proposed in this study. Driver behavior can be modeled as a binary decision which is stop or go at the onset of amber. Field data were recorded from three intersections in Taiwan (one in Taipei city, two in Taichung city). The data were analyzed through the factor analysis and logit model estimation techniques. The BPN model developed for each intersection consisted of two input variables in the input buffer and one driver's decision in the output layer. The results obtained from the study show the applicability and validation of the basic ANN method to the complex driver behavior problem. The relatively simple BPN model turns out to be a very satisfactory tool for predicting accurate network outcomes compared to the logit model. It is expected that the validated model can be directly applied to traffic controls in urban areas.