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The travel demand forecasting model plays a crucial role in evaluatinglarge-scale infrastructure projects, such as the construction of new roads ortransit lines. While combined modeling approaches have been explored as asolution to overcome the problem of input and output discrepancies in asequential four-step modeling process, previous attempts at combined modelshave encountered challenges in real-world applications, primarily due to theirlimited behavioral richness or computational tractability. In this study, wepropose a novel convex programming approach and present a key theoremdemonstrating that the optimal solution is the same as the one from solving thehierarchical extended logit model. This model is specifically designed tocapture correlations existing in travelers' choices, including similaritiesamong transport modes and route overlaps. The convex property of our modelensures the existence and uniqueness of solutions and offers computationalefficiency. The advantages of our proposed model are twofold. Firstly, itprovides a single unifying rationale (i.e., utility maximization) that is validacross all steps. Secondly, its combined nature allows a systematic approach tohandling observed data, enabling a more realistic representation of reality. Byaddressing the limited behavior richness of previous combined models, ourconvex programming-based approach shows promise in enhancing the accuracy andapplicability of travel demand forecasting, thereby aiding in the planning anddecision-making processes for infrastructure projects.

A Combined Convex Model for Travel Demand Forecasting with Hierarchical Extended Logit Model