EURO 2024 Copenhagen
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2920. A Combined Convex Formulation Replacing Hierarchical Extended Logit Model for Travel Demand Forecasting

Invited abstract in session WC-38: Advances in algorithms and applications for linear and convex optimization, stream Conic Optimization: Theory, Algorithms, and Applications.

Wednesday, 12:30-14:00
Room: 34 (building: 306)

Authors (first author is the speaker)

1. Youngseo Kim
Civil and Environmental Engineering, Cornell University
2. Damon Wischik
Computer Science and Technology, University of Cambridge
3. Soroosh Shafieezadeh Abadeh
Operations Research and Information Engineering, Cornell University
4. Gioele Zardini
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology
5. Samitha Samaranayake
UC Berkeley

Abstract

The travel demand forecasting model plays a crucial role in evaluating large-scale infrastructure projects, such as the construction of new roads or transit lines. While combined modeling approaches have been explored as a solution to overcome the problem of input/output discrepancies in a sequential four-step modeling process, previous attempts at combined models have encountered challenges in real-world applications, primarily due to their limited behavioral richness or computational tractability. In this study, we propose a novel convex programming approach and present a key theorem demonstrating that the produced optimal solution coincides with the one arising from the hierarchical extended logit model. This model is specifically designed to capture correlations existing in travelers' choices, including similarities among transport modes and route overlaps. The convex property of our model ensures the existence of solutions and naturally offers computational efficiency. The advantages of our proposed model are twofold. First, it provides a single unifying rationale (i.e., utility/entropy maximization) that is valid across all steps. Second, its combined nature allows one to systematically handle observed data, enabling a better representation of reality. Our convex programming approach shows promise in enhancing the accuracy and applicability of travel demand forecasting, thereby aiding in the decision-making processes for infrastructure projects.

Keywords

Status: accepted

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