María Luz López-García | José Ángel Martín Baos

The time slot allocation problem in liberalised passenger railway markets: a multi-objective approach

Thu, 31 Jul 2025 00:00:00 +0000

Un enfoque práctico para la enseñanza de Álgebra y Matemática Discreta mediante clase invertida en Ingeniería Informática

Wed, 09 Jul 2025 00:00:00 +0000

Abstract

The teaching of abstract concepts in technical and scientific subjects poses a recurring challenge in university education, particularly in the early years, where students face complex content and new tools. Specifically, in the course on Algebra and Discrete Mathematics, typically taught in the first year of the Computer Engineering degree, these difficulties are compounded by inconsistent study habits and low class participation. Additionally, the content is often perceived as overly abstract and disconnected from real-world applications. This work describes how the practical sessions of this course have been redesigned through the implementation of a flipped classroom methodology. Interactive notebooks in MATLAB, along with explanatory videos available in both Spanish and English, have been developed to promote autonomous study prior to in-person sessions. During class, students collaboratively solve a case study based on a real-world problem, while the instructor acts as a facilitator, guiding discussions and addressing specific questions. The pilot experience, carried out during the 2023/24 academic year, has shown significant improvements in student motivation, understanding, and performance, creating a more participatory and dynamic learning environment.

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

Tue, 08 Apr 2025 00:00:00 +0000

An Optimization Model for Determining Access Costs in Rail Freight Transportation

Sun, 30 Jun 2024 00:00:00 +0000

Abstract

The liberalization of European railway markets necessitates a vertical separation governance framework, with Infrastructure Managers (IMs) providing resources and Freight Operating Companies (FOCs) utilizing them. Effective pricing of track access between these entities is vital for profitability and system efficiency. Additionally, achieving a modal shift of freight from road to rail is crucial for road safety and EU objectives alignment. This paper introduces a novel pricing methodology for train paths, optimizing infrastructure utilization by IMs while considering competitive dynamics and environmental impacts. It addresses challenges such as elastic demand, integrating network capacity into pricing strategies, and managing non-additive costs and path interdependencies. The proposed model incorporates dynamic demand patterns, temporally variable capacities, and total revenue considerations across the planning horizon. A discretization-based approach, focusing on irregular time intervals and unit freight tonnage, is developed for solving the continuous model. This involves linking freight tonnage units to a prototype train, with a discrete event simulation model employed. The objective function is also discretized, yielding a finite set of optimization variables to be optimized subject to simulation model. An application of this approach is demonstrated through a case study of the Mediterranean Corridor.

Enhancing the convergence speed of line search methods: Applications in Neural Network training

Sun, 30 Jun 2024 00:00:00 +0000

Abstract

The training of machine learning models, such as neural networks, relies on optimisation techniques that necessitate large volumes of data. The algorithms that have demonstrated satisfactory performance on this task frequently use linear searches on subsets of the data. This ensures that, despite the potential low quality of the search direction, the overall computational cost remains low, making this strategy globally efficient. In these methods, strategies employing a constant learning rate have proven to be particularly effective. This paper introduces a novel scheme designed to significantly expedite the convergence process of line search-based methods. Our approach incorporates additional high-quality linear searches derived from the convergence process of the methods, and by making use of an Armijo rule, it dynamically adjusts the step size through successive reductions or expansions based on the evaluated quality of the descent direction. This strategic adjustment enables more substantial progress in the search direction, potentially reducing the number of iterations needed to reach an optimal solution. We have applied our proposed solution to accelerate the performance of widely-used algorithms such as Gradient Descent (GD), Momentum GD, and Adaptive Moment Estimation (Adam). To illustrate the practical implications and effectiveness of our approach, we present a comprehensive case study focusing on the training of Deep Neural Networks and Kernel Logistic Regression.

An optimal control model for determining freight rail transport access costs

Sun, 19 May 2024 00:00:00 +0000

Abstract

The liberalization of European railway markets, as outlined in European Directive EU 91/440/EEC, mandates a vertical separation governance structure within the railway industry. This vertical separation involves the establishment of two distinct entities: the Infrastructure Manager (IM), responsible for providing railway resources, and the Freight Operating Company (FOC), which operates freight services utilizing the infrastructure provided by the IM. Determining track access pricing between these entities is of utmost interest for European countries extensive monitorings are carried out has emerged as a significant and urgent challenge, one that has profound implications not only for the profitability of IMs and FOCs but also for the efficiency and quality of the entire railway system.

Furthermore, this issue is pivotal in achieving the target objectives set by the European Union (EU), which aims to achieve a modal shift away from road freight transport towards more sustainable modes of transportation. Specifically, the EU has set a goal of achieving a modal shift of 30% by 2030 and at least 50% by 2050 for shipments exceeding 300 km. In this context, rail freight transport is widely recognized as a potentially cost-effective and environmentally sustainable alternative due to its ability to realize economies of scale, reduce pollutant emissions, and mitigate other externalities.

In this paper, we propose an optimal control method for pricing train paths so that the IM maximizes the utilization of public funding while promoting competition in the rail freight market and taking into account both environmental impact and road safety.

The methodology presents several challenges. First, the model needs to account for elastic demand to capture the effect of costs and travel times on modal split. For this reason, road freight transportation is simplified, and a modal split is performed using a logit model.

Second, it involves replacing a basic toll per kilometer scheme with one that incorporates the capacity of the railway network into the pricing process. This means that congested routes should be subject to higher charges than less-demand routes. Hence, a dynamic cargo flow model with capacities on arcs has been chosen. The temporal aspect of the model allows for consideration of dynamic demand patterns and temporally variable capacities (day/night).

The third element of the model deals with a non-additive cost structure and interdependence of train paths when sharing railway segments. This has been achieved by pricing the total revenue received for the use of train paths over the entire planning period.

PyKernelLogit: Penalised maximum likelihood estimation of Kernel Logistic Regression in Python

Fri, 01 Mar 2024 00:00:00 +0000

ROBIN: Rail mOBIlity simulatioN

Wed, 06 Sep 2023 00:00:00 +0000

Abstract

The use of simulators is frequent in the study of complex systems. They replicate a real system and allow obtaining data from the simulated process as well as providing a mathematical model that helps answer What If? questions. This opens up the possibility of evaluating laboratory environment techniques and processes that will later be implemented in the real system. Competitive passenger rail services are complex systems. The efficient design of market mechanisms that encourage competition to offer more and better services requires analytical tools on which to test these new policies. This makes it essential to develop a simulator, as real and complete as possible, to carry out this analysis. This paper presents the development of a microscopic simulator, called ROBIN (Rail mOBIlity simulatioN), to simulate rail mobility in a competitive regime. The developed simulator is parameterizable and general enough to simulate passenger flows in competing railway systems. The tests carried out with ROBIN using the Spanish railway market as a case of study are proof of its usefulness, allowing disaggregated modelling of passenger behaviour and their travel choices in a competitive railway market.

The time slots allocation problem in liberalised passenger railway markets: a multi-objective approach

Wed, 06 Sep 2023 00:00:00 +0000

Abstract

The liberalisation of the European passenger railway markets through the European Directive EU 91/440/EEC states a new scenario where different Railway Undertakings (RUs) compete with each other in an auction or bidding process for time slots to optimise its profit. The infrastructure resources are provided by the Infrastructure Manager (IM) who analyses and assesses the bids received, allocating the resources to each RU. Time slots allocation is a fact that drastically influences the market equilibrium and a problem whose resolution makes it possible to ensure competitiveness within the market. In this paper, time slots allocation problem is modeled in the context of a liberalised passenger railway market. Then, the problem is addressed using two approaches: the first one allocates time slots to each company according to a set of priorities, while the second one introduces a criterion of fairness in the treatment of companies to ensure competition. A multi-objective algorithm has been developed and evaluated for its effectiveness in solving the time slots allocation problem in a liberalized high-speed corridor of the Spanish railway network.

Discrete choice modeling using Kernel Logistic Regression

Wed, 01 Jan 2020 00:00:00 +0000

Discrete choice modeling using Kernel Logistic Regression

Wed, 18 Sep 2019 00:00:00 +0000

Abstract

The Kernel Logistic Regression is a popular technique in machine learning. In this work this tech- nique is applied to the field of discrete choice modeling. This approach is equivalent to specifying non-parametric utilities in random utility models. A Monte Carlo simulation experiment has been carried out to compare this approach with Multinomial Logit models, comparing the goodness of fit and the capability of obtaining the specified utilities.