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Investigation into the Positioning Accuracy Required for Traffic Management Systems on Different Types of Railway Services
Abstract
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.
... The parameters of railway systems are diverse due to the diversity of railway networks and services, as mentioned in Section 2.2. Railway services can be classified into three groups which represent the differences in infrastructure, rolling stock and the operational timetable, as shown in Fig. 5.11 [170]. To model a specific railway service, several parameters need to be considered. ...
... Data flow within TMS[170] The TMS process time depends on the optimisation algorithm and the complexity of the railway network and is usually restricted by process time limit, between 60 s[181][185] and 300 s[186][187]. TMS typically predicts changes in the train dynamic data for the average process time. ...
... 11 Railway network and service parameters considered in this experiment, adapted from[170] ...
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.
... In this study, TMS monitors the trains' movement in real-time taking into account the planned times in the trains' timetable. The method used follows that presented in [9] and is briefly described here. ...
... The outcome of applying the optimal and non-optimal TMS solutions on the railway simulator is compared and the total delay is measured for each case. For further details, refer to [9]. In order to study the impact on different network capacity levels, the above procedure is repeated using different timetables. ...
... In this study, the search for a new train order is triggered when the presence of potential conflicts is detected. Potential conflicts are detected when a scheduled train is delayed by more than a pre-defined delay threshold at the entrance of the control area [3,31,37,38]. The expected time of entry to the control area is calculated based on the original conflict-free timetable. ...
Nowadays the railway industry is beginning to give serious consideration to using intelligent traffic management systems (TMSs) in order to improve railway performance regarding train and passenger delays and robust use of capacity. The TMS is responsible for handling railway traffic once a disturbance happens. A fundamental input parameter of a TMS is the train positions, to be used for traffic re‐planning purposes. Inaccuracy in the train positioning data could significantly influence the effectiveness of a TMS. In this study, the authors developed a framework to evaluate how inaccuracies in the train position reporting may affect the TMS performance. This is achieved by assessing the impact of adding inaccuracies to the train position reported to a simulated TMS as it handles operational disturbances in real‐time. The performance of the TMS is analysed by considering variability in overall delay outcomes after re‐planning based on using accurate/inaccurate positional data. They demonstrate the usefulness of their framework in determining the positional accuracy required for the effective application of a basic rescheduling system via an example on a bottleneck area. Results show how the positioning inaccuracies can affect TMS and thus the overall delay.
The suitability of EGNSS (including EGNOS and Galileo early services) for safety railway applications has been analysed by several European projects such as Grail, Grail-2, Satloc, 3InSat, and it is currently being analysed by ERSAT EAV. ERSAT EAV in particular is addressing the following three challenges beyond the state-of-the-art: a) reuse of ETCS odometry by adding the virtual balise concept to eliminate the fixed balise along the line; b) adoption of the public EGNOS Augmentation network “upgraded” with Local augmentation networks to fulfil the railways’ requirements; c) verification and validation of alternative GNSS solutions to guarantee localization functions in areas where the GNSS signal is not available and/or subject to interference.
This paper investigates the effectiveness of global strategies for real-time railway traffic optimization. In the last years, this research area experienced an increasing interest due to the expected growth of traffic and the limited possibilities of enhancing the infrastructure, which increase the needs for efficient use of resources and the pressure on traffic controllers. We carry on our study using an advanced computerized dispatching system, called ROMA (Railway traffic Optimization by Means of Alternative graphs). This system is able to optimize the future evolution of the railway traffic while taking into account accurately the actual train positions and dynamics, as well as railway signaling and operating rules and conditions. Specifically, we compare local versus global optimization strategies for train reordering and rerouting, and estimate the potential of the new concept of dynamic traffic management. An extensive computational study is carried out, based on a dispatching area of the Dutch railway network. We study practical size instances and different types of disturbances, including train delays and blocked tracks. Our results show the high potential of ROMA dispatching system as a support tool to improve punctuality.
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.
This work focuses on the stochastic evaluation of train schedules computed by a microscopic scheduler of railway operations based on deterministic information. The research question is to assess the degree of sensitivity of various rescheduling algorithms to variations in process times (running and dwell times). In fact, the objective of railway traffic management is to reduce delay propagation and to increase disturbance robustness of train schedules at a network scale. We present a quantitative study of traffic disturbances and their effects on the schedules computed by simple and advanced rescheduling algorithms. Computational results are based on a complex and densely occupied Dutch railway area; train delays are computed based on accepted statistical distributions, and dwell and running times of trains are subject to additional stochastic variations. From the results obtained on a real case study, an advanced branch and bound algorithm, on average, outperforms a First In First Out scheduling rule both in deterministic and stochastic traffic scenarios. However, the characteristic of the stochastic processes and the way a stochastic instance is handled turn out to have a serious impact on the scheduler performance.
For pt.I see ibid., vol.49, no.2, p.467-75 (2000). In this paper,
applications of the map matching algorithm proposed in part I are
presented. In particular, steady-state Kalman filters are proposed and
applied for filtering the yaw rate and tachometer signals. In addition,
experimental results, using a quartz yaw rate sensor and axle encoders
aboard a freight train, are included to show the performance of the
proposed map matching algorithm
One approach to reduce energy consumption in railway systems is to implement optimised train trajectories. These are speed profiles that reduce energy consumption without foregoing customer comfort or running times. This is achieved by avoiding unnecessary braking and running at reduced speed whilst maintaining planned arrival times. An optimised train trajectory can be realised using a driver advisory system (DAS). The optimal train trajectory approach needs a variety of input data, such as the train's position, speed, direction, gradient, maximum speed, dwell time, and station locations. Many studies assume the availability of a very accurate train position in real time. However, providing and using high precision positioning data is not always the most cost-effective solution. The aim of this research is to investigate the use of appropriate positioning systems, with regard to their performance and cost specifications, with optimised trajectories. This paper first presents a single train trajectory optimisation to minimise overall energy consumption. It then explores how errors in train position data affect the total consumed energy, with regard to the tractive force due to gradient when following the optimised trajectory. A genetic algorithm is used to optimise the train speed profile. The results from simulation indicate that a basic GPS system for specifying train position is sufficient to save energy via an optimised train trajectory. The authors investigate the effect of error in positioning data, to guarantee the reliability of employing the optimised solution for saving energy whilst maintaining an acceptable journey time.
Railway traffic is often perturbed by unexpected events and appropriate train routing and scheduling shall be applied to minimize delay propagation. A number algorithms for this routing and scheduling problem have been proposed in the literature and they have been tested in different traffic situations. Nonetheless, their performance are almost always studied considering perfect knowledge of future traffic conditions, which is almost impossible to achieve in reality. In this paper, we propose an experimental analysis assessing the usefulness of these algorithms in case of imperfect information. We consider RECIFE-MILP as a traffic management algorithm and advanced or delayed train entrance times in the control area as the source of imperfect information. The results show that the application of traffic management optimization allows outperforming the first-come-first-served management strategy even if the actual traffic conditions are not perfectly known by the optimization algorithm.
In railway operations, if the journey of a preceding train is disturbed, the service interval between it and the following trains may fall below the minimum line headway distance. If this occurs, train interactions will happen, which will result in extra energy usage, knock-on delays, and penalties for the operators. This paper describes a train trajectory (driving speed curve) optimization study to consider the tradeoff between reductions in train energy usage against increases in delay penalty in a delay situation with a fixed block signaling system. The interactions between trains are considered by recalculating the behavior of the second and subsequent trains based on the performance of all trains in the network, apart from the leading train. A multitrain simulator was developed specifically for the study. Three searching methods, namely, enhanced brute force, ant colony optimization, and genetic algorithm, are implemented in order to find the optimal results quickly and efficiently. The result shows that, by using optimal train trajectories and driving styles, interactions between trains can be reduced, thereby improving performance and reducing the energy required. This also has the effect of improving safety and passenger comfort.
It is necessary to provide corresponding wireless communication network plan scheme for China high speed railway development. This paper investigates the quality of service of high speed railway wireless communication system according to carrier-to-interference, bit error rate and handover standard. Simulation results indicate that this communication network could hardly work rightly in urban and suburban; whereas in the rural area it could provide enough quality under some network plan conditions: for rural quasi-open area the cell radius should be less than 7km and the overlap area should be between 3.9km and 5.5km; for rural open area, the cell radius could reach 10km with the overlay area between 4.8km and 8.1km.
In the last decade simulation models and optimization environments have been developed that are able to address the complexity of real-time railway dispatching. Nevertheless, actual implementations of these systems in practice are scarce. Essential for implementation of an advanced dispatching system is the trust of traffic controllers into a stable working of the system. Nervous systems might change advice suddenly, and even switch back to a solution previously discarded, as time and knowledge of the perturbation progress. To this end, we propose several metrics and a framework to assess the stability of railway dispatching solutions under incomplete knowledge, and report on the evaluation of the state-of-the-art dispatching system ROMA, coupled with the simulation environment EGTRAIN, here considered as a surrogate of the real field. Rescheduling plans calculated at different control stages have been compared for different prediction horizons of the rescheduling tool. This setup has been applied to the Dutch Utrecht–Den Bosch corridor. Results show that the instability increases as stochastic disturbances propagate. Shorter prediction horizons give plans which are more stable over time in terms of train reordering, but tend to manage perturbations mostly by retiming. Larger horizons instead allow to manage traffic essentially by reordering trains but lead to more unstable plans. Enlarging the prediction horizon over a given threshold does not alter neither the structure of plans nor their variation over time.
This paper deals with the program for the introduction and exploitation of space technologies based on the ERTMS/ETCS (European Railways Train Management System/European Train Control System) architecture bundling the EGNOS-GNSS infrastructures in the train control system, in order to improve performance and enhancing safety, reducing the investments on the railways circuitry and its maintenance. These solutions can be successfully applied for local and regional lines and low traffic lines, where the issue for a railroad enhanced service, requires increasingly financing to maintain and improve infrastructure. In order to assess the performance and validate the space technologies in a real railways environment, a special focus will be dedicated to the case of the regional lines in Sardinia where is a plan to deploy a Test Site.
China Train Control System level 3 (CTCS-3) transmits bi-directional track-train data via Global System Mobile for Railway (GSM-R) network, and GSM-R network should provide secure wireless environment to ensure train operation safety. GSM-R network handoff will cause a brief interruption of communication. If a train control message is on transmission during this interruption time, some message data units may be erroneously transmitted during the transmission interference and must be corrected by re-transmission. These re-transmissions result in a delay. This paper analysis's the message transfer mechanism, summarizes some factors influence on the message of Movement Authority (MA) transfer delay during a handoff interference, obtains values of transfer delay of MA affected by handoff, and at last verifies that recovery period of GSM-R network required in QoS specification can meet the needs of train control data transmission.
This paper presents the results of a study carried out in the Greater London area to assess
and characterise the performance of the Global Positioning System (GPS) for vehicle
positioning in urban areas. The performance assessment addresses, in varying levels of detail,
the issues of service coverage, positioning accuracy, integrity and availability of service. The
project was supported by Racal Survey Limited and is part of the wider research at Imperial
College in the area of architectural design and testing of Advanced Transport Telematics
(ATT) systems. The results highlight the shortcomings of GPS as the sole means for in-car
navigation in urban areas and details the temporal and spatial considerations to be taken
into account in the process of designing an integrated positioning system capable of meeting
navigation requirements placed on ATT systems.
After a major service disruption on a single-track rail line, dispatchers need to generate a series of train meet-pass plans at different decision times of the rescheduling stage. The task is to recover the impacted train schedule from the current and future disturbances and minimize the expected additional delay under different forecasted operational conditions. Based on a stochastic programming with recourse framework, this paper incorporates different probabilistic scenarios in the rolling horizon decision process to recognize (1) the input data uncertainty associated with predicted segment running times and segment recovery times and (2) the possibilities of rescheduling decisions after receiving status updates. The proposed model periodically optimizes schedules for a relatively long rolling horizon, while selecting and disseminating a robust meet-pass plan for every roll period. A multi-layer branching solution procedure is developed to systematically generate and select meet-pass plans under different stochastic scenarios. Illustrative examples and numerical experiments are used to demonstrate the importance of robust disruption handling under a dynamic and stochastic environment. In terms of expected total train delay time, our experimental results show that the robust solutions are better than the expected value-based solutions by a range of 10-30%.
A standard practice to improve punctuality of railway services is the addition of time reserves in the timetable to recover perturbations occurring in operations. However, time reserves reduce line capacity, and the amount of time reserves that can be inserted in congested areas is, therefore, limited. In this paper, we investigate the new concept of flexible timetable as an effective policy to improve punctuality without decreasing the capacity usage of the lines. The principle of a flexible timetable is to plan less in the timetable and to solve more inter-train conflicts during operations. The larger degree of freedom left to real-time management offers better chance to recover disturbances. We illustrate a detailed model for conflict resolution, based on the alternative graph formulation, and analyze different algorithms for resolving conflicts, based on simple local rules or global optimization. We compare the solutions obtained for different levels of flexibility and buffer time inserted in the timetable. An extensive computational study, based on a bottleneck area of the Dutch railway network, confirms that flexibility is a promising concept to improve train punctuality and to increase the throughput of a railway network.
The paper discusses the current state of research concerning railway network timetabling and traffic management. Timetable effectiveness is governed by frequency, regularity, accurate running, recovery and layover times, as well as minimal headway, buffer times and waiting times. Analytic (queuing) models and stochastic micro-simulation are predominantly used for estimation of waiting times and capacity consumption anlong corridors and in stations, while combinatorial models and stability analysis are suitable for network timetable optimisation. Efficient traffic management can be achieved by real-time monitoring, fusion, analysis and rescheduling of railway traffic in case of disturbances. Real-time simulation, optimisation and impact evaluation of dispatching measures can improve the effectiveness of rescheduling and traffic management. The display of dynamic signal and track occupancy data in driver cabins, as RouteLint developed by ProRail, can support anticipative actions of the driver in order to reduce knock-on delays and increase throughput.
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