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Using Information Engineering to Understand the Impact of Train Positioning Uncertainties on Railway Subsystems
Abstract and Figures
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. Preliminaries ii 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.
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This presentation is a comprehensive review of the use of X-ray computed tomography (CT) for dimensional metrology with an update on the international efforts to create standards for metrological testing and uncertainty assessment. Objectives: i) to know the main advantages of industrial X-ray computed tomography for inspection and quality control ii) to determine which cases industrial X-ray computed tomography can be used to evaluate dimensional information accurately iii) to discover how the X-ray computed tomography technology can be used for metrological traceability with quantifiable measurements.
This work addresses a tactical railway traffic scheduling problem focused on the optimization of train sequencing and routing decisions and timing decisions related to short-term maintenance works in a railway network subject to disturbed process times. This is modeled as a mixed-integer linear programming formulation in which the traffic flow and track maintenance variables, constraints and objectives are integrated under a stochastic environment. The resulting bi-objective optimization problem is to minimize the deviation from a scheduled plan and to maximize the number of aggregated maintenance works under stochastic disturbances. The two objectives require to schedule competitive train operations versus maintenance works on the same infrastructure elements. Computational experiments are performed on a realistic railway network. We measure the quality of the integrated solutions in terms of their robustness to stochastic perturbations of the train travel times and of the maintenance works. Pareto optimal methods are compared for the bi-objective problem. We also evaluate the impact of introducing routing stability constraints in order to force the trains to keep the same route among the different stochastic disturbed scenarios. The experiments show that forcing the routing stability reduces the routing flexibility and the ability to optimize the two performance indicators when dealing with stochastic disturbances.
This paper presents the development of SmartDrive package to achieve the application of energy-efficient driving strategy. The results are from collaboration between Ricardo Rail and the Birmingham Centre for Railway Research and Education (BCRRE). Advanced tram and train trajectory optimization techniques developed by BCRRE as part of the UKTRAM More Energy Efficiency Tram project have been now incorporated in Ricardo's SmartDrive product offering. The train trajectory optimization method, associated driver training and awareness package (SmartDrive) has been developed for use on tram, metro, and some heavy rail systems. A simulator was designed that can simulate the movement of railway vehicles and calculate the detailed power system energy consumption with different train trajectories when implemented on a typical AC or DC powered route. The energy evaluation results from the simulator will provide several potential energy-saving solutions for the existing route. An enhanced Brute Force algorithm was developed to achieve the optimization quickly and efficiently. Analysis of the results showed that by implementing an optimal speed trajectory, the energy usage in the network can be significantly reduced. A driver practical training system and the optimized lineside driving control signage, based on the optimized trajectory were developed for testing. This system instructed drivers to maximize coasting in segregated sections of the network and to match optimal speed limits in busier street sections. The field trials and real daily operations in the Edinburgh Tram Line, U.K., have shown that energy savings of 10%-20% are achievable.
The real-time Railway Traffic Management problem consists in finding suitable train routes and schedules to minimize delay propagation due to traffic perturbations. RECIFE-MILP is a mixed integer linear programming based heuristic for this problem which has proven to be effective in various contexts. However, when instances are very large or difficult, the performance of the algorithm may worsen. In this paper, we propose valid inequalities to boost the performance of RECIFE-MILP. These valid inequalities link the routing and scheduling binary variables. We also provide an instance in which they are able to represent all the facets of the projection of the convex hull of the problem in the subspace of the binary variables. Moreover, they allow reformulating the model based on a reduced number of scheduling binary variables. In an experimental analysis based on realistic instances representing traffic in four French infrastructures, we observe that the merit of addition of valid inequalities depends on the specific case-study at hand, and that the reduction of the number of binary variables in general boosts the performance of RECIFE-MILP significantly.
Nowadays, the digitisation and automation of railway systems are being carried out all over the world, in order to increase the systems’ accuracy, efficiency and reduce maintenance costs. As part of this trend, intelligent traffic management systems (TMS) are under investigation as a way to increase punctuality and automatically return trains back to the original traffic plan when an operational disturbance happens. A TMS needs a variety of input data to consider the current traffic conditions, predict the future state and decide on a new traffic plan when necessary. Numerous studies have proposed TMS designs; all the proposed systems need to read the train positions in real-time for monitoring and analysis purposes. However, the accuracy of the train positions that can be reported in real-time varies; it depends mainly on the control system design and type of positioning sensor used. Train position uncertainty can significantly influence the performance of a proposed TMS, although the impact has rarely been assessed. In this study, TMS positional accuracy requirements for different railway services are investigated. The influence of train positioning uncertainty is studied with respect to TMS of urban, inter-city and high-speed and mixed-traffic services. This is achieved by simulating the characteristics of these railway services in terms of different trains, tracks, a local TMS and train positioning systems with their associated uncertainty. The experiment is carried out first using exact position data; then it is repeated using position data containing stochastic inaccuracies. The TMS outputs are compared with respect to the train order of the traffic plan and the trains’ total delay. The results show that a small positioning deviation can influence the TMS performance of an urban service, while the TMS of high-speed service is affected less by positioning deviation than the TMS of other services.
Integrating new candidate sensors, such as Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU), into fail-safe train positioning systems have recently become a prominent area of research. Although there are a number of contributions related to the design of data fusion algorithms, the lack of details in raw measurements analysis has directly motivated this paper. This paper aims to record data from a variety of sensors (such as GNSS, IMU, magnetometer, barometer, tachometers, and Doppler radars) to evaluate train velocity and railway features (track slope, curve cant, and radius) extending previous works in the instrumentation and measurement field. The field test designed and concisely described in this paper presents several challenging environments, such as a tunnel, which can be used to analyze the candidate sensors limitations. In addition, a demonstration of a data fusion algorithm is presented to calculate train velocity based on measurements from the candidate sensors. The results obtained by an extended Kalman filter using GNSS and IMU are compared with velocity recorded by tachometers and Doppler radars, which is considered to be the reference value. The calculated velocity by IMU and GNSS when both sensors measurements are available presents an absolute error in velocity lower than 2 km/h in more than 90% of test duration. Finally, railway features (curve radius, cant, and slope) are calculated and analyzed according to train and railway dynamics.
Driver Advisory Systems (DAS) aim to provide reliable and efficient driving instructions to the driver with optimized trajectory. This paper studies trajectory optimization method to achieve energy efficiency and punctuality for DAS. Firstly, the design of offline trajectory optimization is studied, using the Genetic Algorithm (GA) to optimize the energy-efficient trajectory. The performance of offline optimization is evaluated by the application of three different driving styles. Online optimization is constructed to dynamically adjust the trajectory when a driving deviation occurs. The combination of offline optimization and online optimization method is simulated as a dynamic trajectory optimization system, to represent a practical solution for DAS systems to achieve energy efficiency and punctuality by real-time adjustment during the journey. An evaluation of energy saving and computation efficiency for the trajectory optimization is conducted based on a simulation of a real-scale route.
A Train Collision Early Warning System (TCEWS) has been developed for collision avoidance. However, there are few studies regarding how to evaluate the collision risk and provide an early warning concerning a preceding train on the railway. In this paper, we have found that the time for collision avoidance is constrained by the timing of events, such as wireless communication latency, driver reaction, safety protection distance and deceleration rate. Considering these timing components, the time to avoid a collision is calculated accurately. To evaluate the potential collision severity when the following train approaches, the collision risk is defined based on the time to avoid a collision. The train collision early warning signal is divided into a four-tier color-coded system based on the collision risk, with red representing the most severe collision risk, followed by orange, yellow and blue. A field test of the train collision early warning strategy on the Hankou-Yichang Railway is analysed. It is demonstrated that the strategy has sufficient capability to indicate a potential collision and warn the following train.