本研究針對包含循序性旅運需求模型四步驟（旅次發生、旅次分布、運具選擇與交通量指派）以及彈性需求函數在內之整合模型為研究對象，藉由超級路網之詮釋，可以將循序性四步驟以及彈性需求合理呈現在同一個延伸性之交通量指派路網之中。本研究採用路徑基礎式的B演算法進行該整合模型之求解，除了利用範例進行數值驗證之外，並與梯度投影法以及Frank-Wolfe演算法的求解績效進行比較。結果顯示B演算法不但在求解績效有較佳的表現，更可提供路網節點及路徑的交通資訊，增加了整合模型在運輸規劃上的應用能力。

This research studies the most complex travel demand forecasting combined model that casts trip generation, trip distribution, mode choice, traffic assignment as well as variable demand functions into a unified framework. By way of a supernetwork representation, this variable demand combined model can be explained as an extended traffic assignment problem. The "B" algorithm is adopted to solve the problem and demonstrated with a small network. Further comparisons among the three selected algorithms (i.e., "B", Frank-Wolfe and Gradient Projection) are made for three test networks. The results show that the "B" algorithm is superior to the Gradient Projection and Frank-Wolfe algorithms in terms of computational time.