Julio Alberto López-Gómez | José Ángel Martín Baos

A Game-based Model for Pricing and Timetabling in Liberalised Passenger Railway Markets

Sun, 12 Jul 2026 00:00:00 +0000

Abstract

We propose a game-theoretic model for pricing and timetabling in liberalised passenger rail markets under vertical separation. Railway undertakings compete for train paths (on-rail competition) and subsequently set fares facing passenger demand (off-rail competition). Passenger choice is modelled through a nested logit structure. The resulting bi-level equilibrium model determines path bids and ticket prices. A column generation algorithm is developed and validated using data from the Spanish HSR Madrid–Barcelona corridor.

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

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

A prediction and behavioural analysis of machine learning methods for modelling travel mode choice

Wed, 01 Nov 2023 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.

A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling

Fri, 11 Mar 2022 00:00:00 +0000

A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling

Wed, 08 Sep 2021 00:00:00 +0000

Abstract

Traditionally, Random Utility Maximization (RUM) models have been widely applied to travel mode choice modelling. Currently, Machine Learning (ML) models are being applied as an alternative to RUM models, since they provide better results in terms of prediction capability and they can manage large volumes of data. In this paper, a comprehensive comparison between classic RUM models and ML models, including single and ensemble classifiers as well as Deep Neural Networks (DNNs), is provided in order to assess systematically the performance of different models over two different datasets which have different sizes and nature of data. Numerical experiments show Random Forest (RF) is the best classifier in terms of accuracy index and the computational cost to train the model.