Search arXivSearch

arXiv · 2504.04257

A model for pricing freight rail transport access costs: economic and environmental perspectives

Abstract

In deregulated railway markets, efficient management of infrastructure charges is essential for sustaining railway systems. This study sets out a method for infrastructure managers to price access to railway infrastructure, focusing on freight transport in deregulated market contexts. The proposed methodology integrates negative externalities directly into the pricing structure in a novel way, balancing economic and environmental objectives. it develops a dynamic freight flow model to represent the railway system, using a logit model to capture the modal split between rail and road modes based on cost, thereby reflecting demand elasticity. The model is temporally discretized, resulting in a mesoscopic, discrete-event simulation framework, integrated into an optimization model that determines train path charges based on real-time capacity and demand. This approach aims both to maximize revenue for the infrastructure manager and to reduce the negative externalities of road transport. The methodology is demonstrated through a case study on the Mediterranean Rail Freight Corridor, showcasing the scale of access charges derived from the model. Results indicate that reducing track-access charges can yield substantial societal benefits by shifting freight demand to rail. This research provides a valuable framework for transport policy, suggesting that externality-sensitive infrastructure charges can promote more efficient and sustainable use of railway infrastructure.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ricardo García-Ródenas, Esteve Codina, Luis Cadarso, María Luz López-García, José Ángel Martín-Baos. 2025-04-05. A model for pricing freight rail transport access costs: economic and environmental perspectives. https://arxiv.org/abs/2504.04257

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Strategic Inference in Stackelberg Games: Optimal Control for Revealing Adversary Intent

We study a continuous-time stochastic Stackelberg game in which a leader seeks to accomplish a primary objective while inferring a hidden parameter of a rational follower. The follower solves an entropy-regularized linear-quadratic tracking problem and responds to the leader's trajectory with a randomized policy. Anticipating this response, the leader designs informative controls to maximize the estimation efficiency for the follower's latent intent, through maximum likelihood estimation. Unlike prior work on discrete-time or finite-candidate inverse learning, our framework enables continuous parameter inference without prior assumptions and endogenizes the information source through the follower's strategic feedback. We derive semi-explicit solutions, prove well-posedness, and develop recurrent neural network algorithms to approximate the leader's path-dependent control. Numerical experiments demonstrate how the leader balances task performance and information gain, highlighting the practical value of our approach for adversarial strategic inference.

math.OC

Stratification for Nonlinear Semidefinite Programming

This paper introduces a stratification framework for nonlinear semidefinite programming (NLSDP) that reveals and utilizes the geometry behind the nonsmooth KKT system. Based on the index stratification of $\mathbb{S}^n$ and its lift to the primal-dual space, a stratified variational analysis is developed. Specifically, we define the stratum-restricted regularity property, characterize it by the verifiable weak second order condition (W-SOC) and weak strict Robinson constraint qualification (W-SRCQ), and interpret the W-SRCQ geometrically via transversality, with stability along strata. The interactions of these properties across neighboring strata are further examined, leading to the conclusion that classical strong-form regularity conditions correspond to the local uniform validity of stratum-restricted counterparts. On the algorithmic side, a stratified Gauss--Newton method with normal steps and a correction mechanism is proposed for globally solving the KKT equation through a least-squares merit function. We demonstrate that the algorithm converges globally to directional stationary points. Moreover, under the second order sufficient condition (SOSC) and the strict Robinson constraint qualification (SRCQ) at an accumulation point, with a suitable correction threshold, the whole sequence converges superlinearly to this point, which is a KKT pair, and eventually identifies the active stratum. The rate is quadratic if the problem data are additionally of class $LC^2$ near the solution.

math.OC

Convergence Rate Analysis of SOAP with Arbitrary Orthogonal Projection Matrices

In this short note, we establish, for the first time, the convergence rate of SOAP, an efficient and popular matrix-based optimizer for training deep neural networks. Our analysis extends to a more general variant of SOAP that admits arbitrary orthogonal projection matrices and requires only that these matrices be conditionally independent of the current stochastic gradient at each iteration. For example, they may be constructed from information available up to the preceding step.

math.OC