Fundamental diagram constrained dynamic optimal transport via proximal splitting methods

TL;DR

This paper introduces a dynamic optimal transport framework constrained by traffic flow principles, enabling modeling of congestion effects and flow saturation in large-scale traffic and flow problems.

Contribution

It develops a convex dynamic optimal transport model incorporating fundamental diagram constraints and proposes proximal splitting algorithms for efficient solution.

Findings

Captures congestion effects through density-dependent flux constraints.

Demonstrates rerouting and congestion-aware spreading in numerical experiments.

Provides a scalable variational framework linking optimal transport and traffic flow theory.

Abstract

Optimal transport has recently been brought forward as a tool for modeling and efficiently solving a variety of flow problems, such as origin-destination problems and multi-commodity flow problems. Although the framework has shown to be effective for many large scale flow problems, the formulations typically lack dynamic properties used in common traffic models, such as the Lighthill-Whitham-Richards model. In this work, we propose an optimal transport framework that includes dynamic constraints specified by the fundamental diagram for modeling macroscopic traffic flow. The problem is cast as a convex variant of dynamic optimal transport, with additional nonlinear temporal-spatial inequality constraints of momentum, modeled after the fundamental diagram from traffic theory. This constraint imposes a density-dependent upper bound on the admissible flux, capturing flow saturation and congestion effects, and thus leaves space for kinetic optimization. The formulation follows the Benamou-Brenier transportation rationale, whereby kinetic energy over density and momentum fields is optimized subject to the mass conservation law. We develop proximal splitting methods, namely the Douglas-Rachford and Chambolle-Pock algorithms, which exploit the separable structure of the constraint set and require only simple proximal operations, and can accommodate additional (time-varying) spatial restrictions or obstacles. Numerical experiments illustrate the impact of the constraint on transport behavior, including congestion-aware spreading, rerouting, and convergence. The framework establishes a connection between optimal transport and macroscopic traffic flow theory and provides a scalable, variational tool for modeling congestion-constricted (or saturation-aware) Wasserstein gradient flow.