刺激-反應方程式之配適，常以取自然對數後再用線性迴歸的方式估計參數，此程 序在計算上較簡單，但必然會損失值為零的數據，因而影響其精確度。 本研究則嘗試採用倒傳遞類神經網路結合遺傳演算法，建構變數與刺激-反應方程式相同之 模式，以避免損失值為零跟車之資料，並改善類神經網路易陷於局部最佳解之困境。 本研究利用電腦虛擬實境技術構建之高速公路，進行跟車資料蒐集，作為訓練與驗證之依據。 分析結果顯示結合倒傳遞網路架構與遺傳演算法的類神經網路，確實可以獲得較低誤差，其 中以一個隱藏層的網路績效較佳；此外，本模式能有效反應不同受測者的跟車行為存在之差 異性。

　　　　　Recently, the focus of research in car-following theory has been on the stimulus-reaction approach. Researchers often use linear regression to calibrate the stimulus-reaction function after processing the original data with nature logarithm. This procedure tends to be less precise when some zero-value data are deleted, although it is computationally convenient. In this study, the composition of artificial neural networks and genetic algorithms are employed to resolve this problem and to improve the performance of artificial neural networks. A prototype freeway driving simulator developed with the virtual reality technique is employed for data collection. These data are used for model calibration and validation. The results show that the combined model is more likely to acquire lower errors than ordinary back propagation neural networks. Networks with one hidden layer tend to perform better. The proposed models may suitably identify different behaviors between different drivers.