Efficient design assessment in the railway electric infrastructure domain using cloud computing

Nowadays, railway infrastructure designers rely heavily on computer simulators and expert systems to model, analyze and evaluate potential deployments prior to their installation. This paper presents the railway power consumption simulator model (RPCS), a cloud-based model for the design, simulation and evaluation of railway electric infrastructures. This model integrates the parameters of an infrastructure within a search engine that generates and evaluates a set of simulations to achieve optimal designs, according to a given set of objectives and restrictions. The knowledge of the domain is represented as an ontology that translates the elements in the infrastructure into an electric circuit, which is simulated to obtain a wide range of electric metrics. In order to support the execution of thousands of scenarios in a scalable, efficient and fault-tolerant manner, this paper introduces an architecture to deploy the model in a cloud environment, and a dimensioning model to find the types and number of instances that maximize performance while minimizing the externalization costs. The resulting model is applied to a particular case study, allowing the execution of over one thousand concurrent experiments in a virtual cluster on the Amazon Elastic Compute Cloud.This work has been partially funded under the grant TIN2013-41350-P of the Spanish Ministry of Economics and Competitiveness, and the COST Action IC1305 ”Network for Sustainable Ultrascale Computing Platforms” (NESUS)

Using information engineering to understand the impact of train positioning uncertainties on railway subsystems

Many studies propose new advanced railway subsystems, such as Driver Advisory System (DAS), Automatic Door Operation (ADO) and Traffic Management System (TMS), designed to improve the overall performance of current railway systems. Real time train positioning information is one of the key pieces of input data for most of these new subsystems. Many studies presenting and examining the effectiveness of such subsystems assume the availability of very accurate train positioning data in real time. However, providing and using high accuracy positioning data may not always be the most cost-effective solution, nor is it always available. The accuracy of train position information is varied, based on the technological complexity of the positioning systems and the methods that are used. In reality, different subsystems, henceforth referred to as ‘applications’, need different minimum resolutions of train positioning data to work effectively, and uncertainty or inaccuracy in this data may reduce the effectiveness of the new applications. However, the trade-off between the accuracy of the positioning data and the required effectiveness of the proposed applications is so far not clear. A framework for assessing the impact of uncertainties in train positions against application performance has been developed. The required performance of the application is assessed based on the characteristics of the railway system, consisting of the infrastructure, rolling stock and operational data. The uncertainty in the train positioning data is considered based on the characteristics of the positioning system. The framework is applied to determine the impact of the positioning uncertainty on the application’s outcome. So, in that way, the desired position resolution associated with acceptable application performance can be characterised. In this thesis, the framework described above is implemented for DAS and TMS applications to understand the influence of positioning uncertainty on their fundamental functions compared to base case with high accuracy (actual position). A DAS system is modelled and implemented with uncertainty characteristic of a Global Navigation Satellite System (GNSS). The train energy consumption and journey time are used as performance measures to evaluate the impact of these uncertainties compared to a base case. A TMS is modelled and implemented with the uncertainties of an on-board low-cost low-accuracy positioning system. The impact of positioning uncertainty on the modelled TMS is evaluated in terms of arrival punctuality for different levels of capacity consumption. The implementation of the framework for DAS and TMS applications determines the following: • which of the application functions are influenced by positioning uncertainty; • how positioning uncertainty influences the application output variables; • how the impact of positioning uncertainties can be identified, through the application output variables, whilst considering the impact of other railway uncertainties; • what is the impact of the underperforming application, due to positioning uncertainty, on the whole railway system in terms of energy, punctuality and capacity

Performance shaping factors affecting driver safety-related behaviour in urban rail systems : Tyne & Wear Metro case

PhD ThesisIt is accepted that train drivers’ safety performance is affected by numerous performance shaping factors (PSF). Design of the physical environment is among these factors. Even though the body of knowledge in rail human factors is increasing, it is limited as it is often i) reactive, ii) focusing mainly on single type incidents, iii) prioritising high profile accidents, iv) not always fully addressing existing risk profiles. Railway systems with different design features are usually grouped together for research purposes thus disregarding the fact that system design can alter effects of the PSFs. This is especially true for urban rail systems. A combination of concurrent and sequential research in this mixed methods thesis has investigated PSFs associated with metro systems design, using the Tyne & Wear Metro system as its application case. The PSFs embedded in everyday operations have been studied on different system levels through historic incident analysis, drivers’ surveys, semi-structured interviews, eye-tracking and simulation experiments. Some of the established methodologies have been adapted in order to address the research objectives set. Novel approaches have been developed for the deployment of in-service eye-tracking using dynamic areas of interest and the development of a low-cost high fidelity simulator using gaming software and hardware. Selected station layouts have been assessed through measures of workload, stress and signal checking behaviour thus supporting PSF inter-dependence. The results suggest the influence on the performance of arrival and departure procedures of the angle between a signal, a driver and a mirror. Among the latent conditions potentially inducing incident propagation are passenger levels, the platform side, informativeness of design elements, openness and lighting conditions of a station, and distances from a stopping position to other elements of the station design.Institute for Sustainability at Newcastle University, through the Sir James Knott and Ridley PhD Scholarshi

Leveraging Connected Highway Vehicle Platooning Technology to Improve the Efficiency and Effectiveness of Train Fleeting Under Moving Blocks

Future advanced Positive Train Control systems may allow North American railroads to introduce moving blocks with shorter train headways. This research examines how closely following trains respond to different throttle and brake inputs. Using insights from connected automobile and truck platooning technology, six different following train control algorithms were developed, analyzed for stability, and evaluated with simulated fleets of freight trains. While moving blocks require additional train spacing beyond minimum safe braking distance to account for train control actions, certain following train algorithms can help minimize this distance and balance fuel efficiency and train headway by changing control parameters

Integrated evaluation of air flow and gas dispersion for underground station safety strategies based on subway climatology

PhD ThesisRail underground systems are seen as a way to overcome traffic congestion in city environments. Many new subways are being built in China and developing countries. Recent studies have however shown that the ventilation of subway systems is poorly understood. There is significant danger to life if a fire occurs or toxins such as chemical or biological agents are released in a subway. Understanding the air flow inside a subway and how this is affected by the local environment is key in establishing effective evacuation strategies. A series of tracer gas experiments conducted as part of this research have been carried out. To expand the subway climatology from an experimental framework into a virtual and simulation environment, 3D Computational Fluid Dynamic models have been developed, which include the simulation of local microclimate and air movement inside the station respectively. The station CFD model has allowed the analysis of the air flow inside the station under the prevailing external weather condition. Results show promising links between external climatic factors, the subway climatology and the ability to predict the dispersal of smoke/toxins. The local weather pattern has a large influence on the background airflow inside a station and dominated the flow direction at station exits which is been used to evaluate the efficiency of pedestrian evacuation and also determine the safer evacuation route and exit. The possibilities of integrating these findings will allow for a more holistic safety assessment to be carried out that could reduce the loss of life or mitigate harmful effects on public health. It also fills a knowledge gap in design guidelines from a safety perspective underground station construction and ventilation

A hybrid Delphi-AHP multi-criteria analysis of Moving Block and Virtual Coupling railway signalling

The railway industry needs to investigate overall impacts of next generation signalling systems such as Moving Block (MB) and Virtual Coupling (VC) to identify development strategies to face the forecasted railway demand growth. To this aim an innovative multi-criteria analysis (MCA) framework is introduced to analyse and compare VC and MB in terms of relevant criteria including quantitative (e.g. costs, capacity, stability, energy) and qualitative ones (e.g. safety, regulatory approval). We use a hybrid Delphi-Analytic Hierarchic Process (AHP) technique to objectively select, combine and weight the different criteria to more reliable MCA outcomes. The analysis has been performed for different rail market segments including high-speed, mainline, regional, urban and freight corridors. The results show that there is a highly different technological maturity level between MB and VC given the larger number of vital issues not yet solved for VC. The MCA also indicates that VC could outperform MB for all market segments if it reaches a comparable maturity and safety level. The provided analysis can effectively support the railway industry in strategic investment planning of VC

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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