Improved Partial Linearization Algorithm for Solving the Combined Travel-Destination-Mode-Route Choice Problem

Abstract

Combined travel demand models (CTDM) that integrate trip generation, trip distribution, modal split, and traffic assignment have been developed to resolve the inconsistency problem between the level-of-service and flow values of the sequential four-step travel demand forecasting procedure. In this paper, an improved partial linearization algorithm for solving the logit-based combined travel-destination-mode-route choice model formulated as a convex mathematical programming is developed. The improvements mainly focus on exploring recent advances in line search strategies to minimize the computational efforts required to determine a suitable stepsize that guarantees convergence. Specifically, the quadratic interpolation and the self-regulated averaging schemes are examined. Numerical results show that the self-regulated averaging line search scheme is more effective and efficient for solving the convex mathematical programming with a complex objective function in terms of solution quality and computational effort.