Compressed Traffic Assignment with the Augmented Lagrangian Method

TL;DR

This paper introduces a path-based compression framework for large-scale traffic assignment problems, utilizing SVD and augmented Lagrangian methods to reduce problem size while maintaining solution quality.

Contribution

It develops a novel compression approach combining path partitioning, low-dimensional SVD representation, and an augmented Lagrangian solver for efficient large-scale traffic assignment.

Findings

Moderate compression achieves significant dimension reduction with high accuracy.

Aggressive compression increases iteration counts and solution gaps.

Rank sensitivity analysis shows limited benefits beyond moderate rank.

Abstract

We consider large-scale traffic assignment problems and develop a path-based compression framework. In particular, we partition paths into major and minor paths according to a set of nominal flows and a prescribed threshold, and retain the major paths explicitly. For the minor paths, we introduce a low-dimensional representation based on a truncated singular value decomposition of the minor path-link incidence matrix. We also provide a feasibility safeguard that ensures the compressed problem remains feasible. To solve the resulting formulation, we use an augmented Lagrangian method with separate penalty parameters for the different constraints and adaptive penalty parameter updates. We report computational studies using the Chicago Sketch, Chicago Regional, and Philadelphia networks. The results show a compression-accuracy trade-off: moderate thresholds can achieve substantial dimension reduction while maintaining high link-flow fidelity, whereas more aggressive compression tends to increase iteration counts and objective gaps. In our rank-sensitivity experiments, increasing the compression rank beyond moderate values produces little improvement in solution quality while increasing computational cost substantially. Overall, the proposed framework offers a practical way to reduce the dimensionality of large path-based traffic assignment problems while preserving feasibility and good solution quality.