Precise vehicle location as a fundamental parameter for intelligent selfaware rail-track maintenance systems

The rail industry in the UK is undergoing substantial changes in response to a modernisation vision for 2040. Development and implementation of these will lead to a highly automated and safe railway. Real-time regulation of traffic will optimise the performance of the network, with trains running in succession within an adjacent movable safety zone. Critically, maintenance will use intelligent trainborne and track-based systems. These will provide accurate and timely information for condition based intervention at precise track locations, reducing possession downtime and minimising the presence of workers in operating railways. Clearly, precise knowledge of trains’ real-time location is of paramount importance. The positional accuracy demand of the future railway is less than 2m. A critical consideration of this requirement is the capability to resolve train occupancy in adjacent tracks, with the highest degree of confidence. A finer resolution is required for locating faults such as damage or missing parts, precisely. Location of trains currently relies on track signalling technology. However, these systems mostly provide an indication of the presence of trains within discrete track sections. The standard Global Navigation Satellite Systems (GNSS), cannot precisely and reliably resolve location as required either. Within the context of the needs of the future railway, state of the art location technologies and systems were reviewed and critiqued. It was found that no current technology is able to resolve location as required. Uncertainty is a significant factor. A new integrated approach employing complimentary technologies and more efficient data fusion process, can potentially offer a more accurate and robust solution. Data fusion architectures enabling intelligent self-aware rail-track maintenance systems are proposed

An Approach for Actions to Prevent Suicides on Commuter and Metro Rail Systems in the United States, MTI Report 12-33

The primary goals of this report are to discuss measures to prevent suicides on commuter and metro rail systems, and to outline an approach for suicide prevention on rail systems. Based on existing literature and analysis of data obtained from the Metrolink system in Southern California, it was found that most suicides occur near station platforms and near access points to the track. Suicides occurred most frequently when relatively more trains were in operation and in areas of high population density. There do not appear to be suicide “hot spots” (e.g., linked to mental hospitals in the proximity, etc.), based on data analyzed for U.S. systems. The suicide prevention measures range from relatively inexpensive signs posting call-for-help suicide hotline information to costly platform barriers that physically prevent people from jumping onto tracks in front of trains. Other prevention measures fall within this range, such as hotlines available at high frequency suicide locations, or surveillance systems that can report possible suicide attempts and provide the opportunity for intervention tactics. Because of the relatively low number of suicides on rail systems, as compared to the overall number of suicides in general, a cost-effective strategy for preventing suicides on rail systems should be approached in a very focused manner. The prevention measures executed by the rail authorities should be focused on the suicides occurring on the rail systems themselves, while the broader problem of suicides should be left to community-based prevention efforts. Moreover, prevention measures, such as surveillance and response, could “piggyback” on surveillance and response systems used for other purposes on the rail systems to make such projects economically feasible

In loco intellegentia: Human factors for the future European train driver

The European Rail Traffic Management System (ERTMS) represents a step change in technology for rail operations in Europe. It comprises track-to-train communications and intelligent on-board systems providing an unprecedented degree of support to the train driver. ERTMS is designed to improve safety, capacity and performance, as well as facilitating interoperability across the European rail network. In many ways, particularly from the human factors perspective, ERTMS has parallels with automation concepts in the aviation and automotive industries. Lessons learned from both these industries are that such a technology raises a number of human factors issues associated with train driving and operations. The interaction amongst intelligent agents throughout the system must be effectively coordinated to ensure that the strategic benefits of ERTMS are realised. This paper discusses the psychology behind some of these key issues, such as Mental Workload (MWL), interface design, user information requirements, transitions and migration and communications. Relevant experience in aviation and vehicle automation is drawn upon to give an overview of the human factors challenges facing the UK rail industry in implementing ERTMS technology. By anticipating and defining these challenges before the technology is implemented, it is hoped that a proactive and structured programme of research can be planned to meet them

Multi-objective model for optimizing railway infrastructure asset renewal

Trabalho inspirado num problema real da empresa Infraestruturas de Portugal, EP.A multi-objective model for managing railway infrastructure asset renewal is presented. The model aims to optimize three objectives, while respecting operational constraints: levelling investment throughout multiple years, minimizing total cost and minimizing work start postponements. Its output is an optimized intervention schedule. The model is based on a case study from a Portuguese infrastructure management company, which specified the objectives and constraints, and reflects management practice on railway infrastructure. The results show that investment levelling greatly influences the other objectives and that total cost fluctuations may range from insignificant to important, depending on the condition of the infrastructure. The results structure is argued to be general and suggests a practical methodology for analysing trade-offs and selecting a solution for implementation.info:eu-repo/semantics/publishedVersio

Learning from accidents : machine learning for safety at railway stations

In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry