An intelligent framework and prototype for autonomous maintenance planning in the rail industry

This paper details the development of the AUTONOM project, a project that aims to provide an enterprise system tailored to the planning needs of the rail industry. AUTONOM extends research in novel sensing, scheduling, and decision-making strategies customised for the automated planning of maintenance activities within the rail industry. This paper sets out a framework and software prototype and details the current progress of the project. In the continuation of the AUTONOM project it is anticipated that the combination of techniques brought together in this work will be capable of addressing a wider range of problem types, offered by Network rail and organisations in different industries

A game theoretic model for re-optimizing a railway timetable

The Setting In the Nineties of the last century the European Commission decided to open the railway market to competition, allowing different railway companies to operate on the same network. Under this framework Infrastructure Managers have to allocate capacity in order to define the timetable, dealing with possible slot conflicts between competing Transport Operators. The Problem An efficient train scheduling requires collecting a lot of information from the Transport Operators, but it may not be in their interests to reveal their private information. Therefore, it may be useful for real-world applications to design methods that provide incentives to Transport Operators for cooperating with the aim of increasing their utility; moreover, this may result in an improvement of the efficiency even for the Infrastructure Managers, so they also have incentives for favouring the cooperation. The Proposal In this paper we propose a game theoretical model in which the agents (Transport Operators) exchange information on their needs and are compensated by a possible increasing of their utility. This approach represents the situation as a coalition formation problem. In particular, we refer to the C-Solution proposed by Gerber (Rev Econ Design 5:149–175, 1), which is applied to some examples, each with different features. This model requires that information is revealed to a small number of competitors. This is rather important in a market currently still characterized by operator reluctance to an indiscriminate diffusion of information. Furthermore, the low dimension of the problem allows having a low computational complexity

Genetic based discrete particle swarm optimization for elderly day care center timetabling

The timetabling problem of local Elderly Day Care Centers (EDCCs) is formulated into a weighted maximum constraint satisfaction problem (Max-CSP) in this study. The EDCC timetabling problem is a multi-dimensional assignment problem, where users (elderly) are required to perform activities that require different venues and timeslots, depending on operational constraints. These constraints are categorized into two: hard constraints, which must be fulfilled strictly, and soft constraints, which may be violated but with a penalty. Numerous methods have been successfully applied to the weighted Max-CSP; these methods include exact algorithms based on branch and bound techniques, and approximation methods based on repair heuristics, such as the min-conflict heuristic. This study aims to explore the potential of evolutionary algorithms by proposing a genetic-based discrete particle swarm optimization (GDPSO) to solve the EDCC timetabling problem. The proposed method is compared with the min-conflict random-walk algorithm (MCRW), Tabu search (TS), standard particle swarm optimization (SPSO), and a guided genetic algorithm (GGA). Computational evidence shows that GDPSO significantly outperforms the other algorithms in terms of solution quality and efficiency

Train service timetabling in railway open markets by particle swarm optimisation

Railway timetabling is an important process in train service provision as it matches the transportation demand with the infrastructure capacity while customer satisfaction is also considered. It is a multi-objective optimisation problem, in which a feasible solution, rather than the optimal one, is usually taken in practice because of the time constraint. The quality of services may suffer as a result. In a railway open market, timetabling usually involves rounds of negotiations among a number of self-interested and independent stakeholders and hence additional objectives and constraints are imposed on the timetabling problem. While the requirements of all stakeholders are taken into consideration simultaneously, the computation demand is inevitably immense. Intelligent solution-searching techniques provide a possible solution. This paper attempts to employ a particle swarm optimisation (PSO) approach to devise a railway timetable in an open market. The suitability and performance of PSO are studied on a multi-agent-based railway open-market negotiation simulation platform

A literature review of Artificial Intelligence applications in railway systems

Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges

Development of an upgrade selection process for railway renewal projects

Currently, many railway systems need to be upgraded to meet the demand for rapidly increasing railway capability, environmental concerns and customer satisfaction, while there is a lack of the right models and tools required to support the early decision making stage of railway renewal projects. In this thesis, a new railway selection upgrade process is proposed, which aims to support early stage decision-making in railway renewal projects by finding the most appropriate solutions to take forward for more detailed consideration. The railway selection upgrade process consists of modelling, simulation, split into macros-assessment and micro-simulation, and evaluation. A high-level feasibility analysis model is developed for the macro-assessment, to help engineers efficiently select the most promising upgrade options for further detailed consideration using microscopic simulation. This process provides a quick and efficient way to quantify evaluation functions, based on the 4Cs (capacity, carbon, customer satisfaction and cost) framework, to give a final suggestion on the most appropriate upgrade options. Two case studies, based on the East Coast Main Lines and the Northern Ireland railway network, are presented in order to demonstrate the application and verify the feasibility of the high-level feasibility analysis model and the railway upgrade selection process

Development of an upgrade selection process for railway renewal projects

Currently, many railway systems need to be upgraded to meet the demand for rapidly increasing railway capability, environmental concerns and customer satisfaction, while there is a lack of the right models and tools required to support the early decision making stage of railway renewal projects. In this thesis, a new railway selection upgrade process is proposed, which aims to support early stage decision-making in railway renewal projects by finding the most appropriate solutions to take forward for more detailed consideration. The railway selection upgrade process consists of modelling, simulation, split into macros-assessment and micro-simulation, and evaluation. A high-level feasibility analysis model is developed for the macro-assessment, to help engineers efficiently select the most promising upgrade options for further detailed consideration using microscopic simulation. This process provides a quick and efficient way to quantify evaluation functions, based on the 4Cs (capacity, carbon, customer satisfaction and cost) framework, to give a final suggestion on the most appropriate upgrade options. Two case studies, based on the East Coast Main Lines and the Northern Ireland railway network, are presented in order to demonstrate the application and verify the feasibility of the high-level feasibility analysis model and the railway upgrade selection process

Evaluation of railway performance through quality of service

Railways worldwide have experienced unprecedented growth over the last decade. The infrastructure of many European railways is becoming increasingly saturated, while the operators face strong demands to increase services and carry more passengers and freight. There has been a high demand for performance improvement, to provide better service to customers. This problem is drawing increasing attention from many parties within and beyond the railway industry. In this research, the author first reviewed and analysed the existing performance measures used in railway transportation. A new generic framework for the measurement and improvement of railway network performance has been proposed, based on the concept of quality of service. The key factors affecting quality of service are identified and analysed. Secondly, the quantification of performance has been identified based on the Quality of Service framework. Multi criteria decision making has been applied to determine the weights of each Key Performance Indicator in the framework. Finally, factors within the railway system have been analysed for their impact on performance with a sensitivity analysis, to support the development of performance improvement plans. A case study has been conveyed to show the influence of the system properties on the performance measured by quality of service

Spécificité du secteur ferroviaire et libéralisation: la question du signal prix

Since the beginning of the 90s, reforming infrastructures has been high on the European Commission’s political agenda. Telecommunication, energy distribution and rail transportation have been liberalized following the same model: separation of network and operation, introduction of competition on operation and setting up of a regulatory agency to monitor the sector.The goal of these liberalization processes is to introduce more economic efficiency into these industries. The vertical separation, core of the liberalization process, and the introduction of competition on operations allow the introduction of a “price signal” which was lacking in the previous institutional arrangement (a vertically integrated state owned monopoly). But is this model initially designed for the telecom industry well fitted for the rail industry? May the price signal be efficient in this industry?We demonstrate that the assumptions underlying the current institutional project of the European Commission (width of the natural monopoly, use of market mechanisms to attribute rail capacity or to set the prices of passenger transportation services) are challenged by the specificities of the rail sector.In order to establish a real competition in this industry, public authorities should perform an important effort of market design and of ex ante regulation. As a minimum requirement, track access charging scheme should be modified, capacity attributed to new entrants should be allocated in a coherent way so they can optimize rolling stock rotations. Purchase and resale of high speed rolling stocks should also be made easier.Depuis le début années 1990, la Commission européenne a entrepris de reformer les grands réseaux d’infrastructure. Les télécommunications, la distribution d’énergie et le transport ferroviaire ont été libéralisés sur un modèle identique : séparation de l’infrastructure et des services, introduction de la concurrence pour les services et mise en place d’une autorité de régulation.Le but de ces libéralisations est d'introduire une plus grande efficience économique dans ces secteurs. La séparation verticale, clef de voute de la libéralisation, et l’introduction de la concurrence à l’aval conduisent à la mise en place d’un « signal prix » qui n’existait pas dans le schéma institutionnel précédent (un monopole verticalement intégré). Mais ce modèle de libéralisation, initialement développé pour les télécommunications, est-il adapté au secteur ferroviaire ? Quelle peut être l'efficacité du signal prix dans ce secteur?La thèse démontre que les hypothèses économiques sous-jacentes au schéma de réformes de la Commission européenne (étendu du monopole naturel, utilisation de mécanismes de marché pour attribuer les capacités ou établir les tarifs des services de transport de passagers) sont mises à mal par les spécificités du secteur.Pour qu’une réelle concurrence s’établisse dans le secteur, les pouvoirs publics doivent effectuer un important travail de « market design » et de régulation ex ante. Il faut a minima que la tarification de l’infrastructure soit modifiée, que les capacités attribuées aux nouveaux entrants soient cohérentes afin qu’ils puissent optimiser leur matériel roulant et que l’acquisition et la revente du matériel roulant grande vitesse soient facilitées