PLASA – Smart Planning and Safety for a safer and more robust European railway sector : D2.1 – Summary of state-of-the-art in simulation

Summary of state-of-the-art in simulationS2R CCA Plasa Smartplanning and safet

A strategy for putting methods in to practice and a formal evaluation of

The overall aim of the ON-TIME project is to improve railway customer satisfaction through increased capacity and decreased delays for both passengers and freight. This is achieved through new and enhanced methods, processes and algorithms. This document is one of the final deliverables of the ON-TIME project, and is produced as an output of Work Package 2: Examination of existing approaches and specification of innovations. The aim of the document is to report on ‘How to implement developed methods into practice’ (Task 2.4) and ‘To collect results and evaluate demonstrators’ (Task 2.5). Chapter 2 details the objectives and expected results in the project. The Technology Readiness Levels (TRLs) of the project innovations before the project start are described. The four demonstration locations, namely the East Coast Main Line, Iron Ore Line, Bologna Node and Netherlands network are briefly described in terms of their traffic types and levels and infrastructure. Chapter 3 provides an overview of the HERMES simulation platform that has been used throughout the project. The evaluation tool which has been developed to undertake quantitative evaluation of the performed simulations is also explained, together with the measures and processes used to provide a quantitative comparator between solutions. Chapter 4 summarises the innovations developed in the project for methods and algorithms, tooling and system integration. These were specified in the original project proposal, and form the key technical outputs of the project. Each innovation is described in terms of its: (i) objectives; (ii) research activities; (iii) developed algorithms and systems; (iv) tests and demonstrations; and (v) evaluations and results. Chapter 5 explains the demonstration systems, simulations and demonstrations which have been undertaken in the project. Four key demonstrations were selected during the first phase of the project. The specific demonstrators were selected to allow the developed innovations to be tested on a range of scenarios from across Europe. Chapter 6 discusses how the results of the project can be put into practice, while Chapter 7 provides a summary of the research undertaken, the achieved TRLs and future tasks. Chapter 7 summarises the six innovations developed in the project and the demonstration on the Iron Ore Line, Sweden. Each innovation and the demonstration is described in terms of its: (i) state-of-the-art; (ii) research outputs; (iii) deliverables and proceedings; (iv) future tasks.On-Tim

Evaluation of field calibration methods and performance of AQMesh, a low-cost air quality monitor.

Field calibrations of NO2, NO, and PM10 from AQMesh Air Quality Monitors (AQMs) were conducted during a summer and an autumn period in a busy street in a midsize Swedish city. All the three linear calibration procedures studied (postscaled, bisquare, and orthogonal data) significantly reduced the ranges and magnitudes of the performance indicators to yield more reliable results than the raw data. The improvements were sufficient to satisfy the European Union (EU) Data Quality Objective (DQO) for indicative measurements as compared to reference data only for NO2 (above 50 µg m-3) and NO (above 30 µg m-3) during the autumn calibration period. The relatively simple bisquare procedure had the best performance overall. The bisquare procedure improved the root mean square error by the same amount as other studies using complex multivariate calibration methods. Low concentrations of pollutants were measured, far below the EU Environmental Quality Standard thresholds and even satisfying the future goals for the Environmental Quality Objectives. Cleaning the raw data by removing data points in the reference data that were below the reference station limit of detections (and the synchronous data points in the AQM prescaled data) was found to improve the performances of the calibration procedures appreciably. Many NO2 and almost all PM10 data points in this study fell below the AQM limit of detection. These low concentrations will probably be a common problem in many field studies, at least in areas with relatively low air pollution. However, the relative errors were sufficiently low for these data points that they could be interpreted as accurately representing low concentrations and did not need to be removed from the datasets. For the NO2 measurements, a slight periodic error correlated with sunlight and increased ambient temperature was noted. NO measurements correlated strongly with increased traffic

D3.1 Improved timetable planning

In ARCC project work package 3, research and innovation activities have been done to identify areas with a need for improved timetable planning methods and outline how new methods can be developed and implemented. Improved timetable planning scope were described and there was an activity to connect to other relevant Shift2Rail projects. An workshop was organised in Stockholm 2018-05-29. State of the art in practice was described for timetable planning in Sweden, UIC 406 method and Ansaldo STS Traffic management systems. Also state of the art in algorithms was described. Future work plan research needs areas are: 1. Understanding of various goals for timetabling and how they co-variate 2. Residual capacity 3. Connection and coordination of the planning processes 4. Connection and coordination of the yard/terminal planning and network planning 5. Integration of freight trains into the timetable, focusing on short-term and ad-hoc 6. Integration of maintenance scheduling and timetabling, at all planning stages 7. Improved decision support for handeling of deviations from timetable in operations 8. Features of planning tools, and implementation of automatized timetablingPeterson är "main editor".Övriga författare är "Medarbetare/bidragsgivare".Automated Rail Cargo Consortium: Rail freight automation research activities to boost levels of quality, efficiency and cost effectiveness in all areas of rail freight operation

Description of a decision support tool aimed at advanced Real Time Network Management and requirements for a demonstrator

In this report we outline a conceptual demonstrator for advanced real time network management for freight rail traffic. The focus is on the coordination between traffic control, train drivers and yard management, three essential parts in the real time management of a rail freight network. The intention is that the demonstrator can support multiple purposes, such as education and training, demonstrating research advancements, and enabling feedback between practitioners, system developers and researchers. The proposed demonstrator has a focus on the interaction between different systems and between humans using these systems, but also on the rail freight system perspective by the inclusion of the connection between the line and the yard. We present a generic architecture and propose existing components that could be combined to such a demonstrator. Thus, even though the demonstrator may seem complex and visionary, the existence of these components makes the realization of the demonstrator realistic. The development roadmap for the demonstrator proposes both a step-wise implementation plan of the complete demonstrator, as well as several partial packages that provide useful sub-demonstrators by themselves. The appendices of the report include contributions to the continued development of two of the components that are part of the demonstrator. Firstly, in order to also better understand the type of situations that yard managers need to handle in operations and what implications these have on the traffic on the line, a Swedish case study has been conducted and the results are presented in Appendix A. More specifically, the case study analyses the factors that influence the departure time deviation for freight trains and how these can be used for predicting the actual departure time. These predictions can be used in a decision support system for yard planning at larger marshalling yards. A conclusion is that no single factor can fully explain the departure time deviation, but many different factors contribute to it, like destination, time of day, train load, number of wagons on the yard, connection time for wagons, and connection time for locomotives. Secondly, to support the traffic controllers and dispatchers with an advanced decision support tool for deviation handling, a selection of different functionalities and algorithms may be required. In Appendix B, two different approaches for disturbance management are presented. Approach 1 (ALG1) is a heuristic, parallel algorithm, while the second approach (ALG2) is an exact algorithm based on state-of-the-art commercial optimization software. In order to classify and evaluate alternative algorithms for train re-scheduling and disturbance management, an assessment framework is also proposed in Appendix B. Based on this framework, the overall strengths and shortcomings of the two mentioned train rescheduling algorithms are assessed while applied on a set of 30 simulated disturbance scenarios of various complexity. The results show that typically, ALG2 obtained good rescheduling solutions for all 30 disturbances, but compared to ALG1, ALG2 is slow in obtaining solutions.ALG1 is good at quickly finding solutions with less passenger delays while it is less effective when it is used to solve disturbances associated with an infrastructure failure. The strength of ALG2 is its ability to reschedule the traffic during infrastructure failures. A detailed presentation of the evaluation is found in Appendix B.H2020 Grant Agreement 826206Deliverable D3.2 of project Fr8Rail II</p

Demonstration of enhanced and integrated line- and yard planning and possibilities for implementation

This document constitutes Deliverable D2.3 of the project FR8RAIL III Work Package (WP) 2 “Real-Time Network Management” within the framework of the Technology Demonstrator - TD5.2 “Digital Transport Management” of IP5 “Technologies for Sustainable &amp; Attractive European Rail Freight” in Shift2Rail. The report documents the conducted work and results from task T2.3 and is the final deliverable from WP2.  Three methods and tools have been developed, analysed, and demonstrated on real-world operational data. These tools address three different but equally important aspects for improving the efficiency of real-time network management of railways and contribute to closing the gap between planning and operations by enabling the traffic management to have a more proactive way of working . The focus of the work is on the coordination of freight operations at lines and marshalling yards, namely: • Coordination of all operational activities taking place at arrival/departure yards. • Replanning of timetables for line traffic. • Prediction of system effects and the combined operations of yards and lines.  Firstly, an integrated demonstration platform for planning operational activities at a marshalling yard is studied. The developed Yard Coordination System (YCS) itself is described as well as how it has been applied and demonstrated in a workshop with experienced participants from the three principally involved stakeholders. The demonstration has shown that a tool like YCS can improve transparency and enable cooperative and pro-active planning. The practitioners reckoned that the tool could prevent and alleviate departure delays, and they expressed a strong wish for continued development of such support. An extensive list of experiences, development suggestions, potentials and risks are reported.  Secondly, a timetable modification module (TIMO) for short-term replanning of line traffic is evaluated. The method uses a heuristic approach that aims at achieving a high bottleneck robustness, which together with algorithm runtime and several other criteria (train path deviation, change in departure time etc) are used in the evaluation. The effect of several parameters in TIMO are studied, such as iteration settings, size of allowed time windows and share of other train paths that may be adjusted—both for peak and off-peak traffic. Furthermore, how TIMO can be used in an iterative procedure to solve the replanning problem on the line in case of ad-hoc maintenance at the departure marshalling yard is demonstrated. The results show that TIMO’s performance depends greatly on various parameter settings, which delimits the (current) use cases for TIMO.  Thirdly, a proof-of-concept model framework for increasing the predictability of yard departures and arrivals is evaluated. The model framework incorporates a machine learning-based yard departure deviation prediction model (YPM) into a macroscopic network simulation model (Proton).  Both the infrastructure manager and the yard operator can benefit from this model framework; the former by getting a more realistic picture of the train that runs along the line, the latter by improved yard arrival estimations.  Finally, the possibilities for real-time usage of these methods and tools are discussed along with their respective impact on the three system level performance indicators load factor, punctuality and average (transportation) speed. FR8RAIL II

fr8hub WP3 Deliverable 3.1 State-of-the-art and specification of innovations, demonstrations and simulations (Shift2Rail)

### The deliverable is available online here: ### Link 1: https://cordis.europa.eu/project/id/777402 ### Link 2: https://projects.shift2rail.org/s2r_ip5_n.aspx?p=FR8HUB ### Abstract: The main aspects of the Shift2Rail project fr8hub are to increase efficiency at the nodes, hubs and terminals in the freight railway system. In addition, the impact of increasing freight train speeds on line capacity will be investigated. Furthermore, the development of freight locomotives of the future will be continued. WP3 delivers a demonstrator showcasing the effects of 1) improved traffic management through better interaction between line and yard, and 2) increased freight speed and its effects on overall increased capacity, punctuality and reduced travel time for both passenger and freight trains. To evaluate and validate the effects, two important lines on the Scandinavian-Mediterranean Corridor in the TEN-T network (Malmö - Hallsberg and Karlsruhe - Basel) in Sweden and Germany are used as case studies. ### fr8hub, Real-time information applications and energy efficient solutions for rail freight; project funded from the European Union's Horizon 2020 research and innovation programme (IP5 - freight

Deliverable D 3.1: Analysis of the gap between daily timetable and operational traffic

Fr8Rail II/Work-Package 3 Real-time network management and improved methods for timetable planning addresses the problem to improve capacity and punctuality in the railway system by developing concepts and methods for tactical planning and operational traffic. In this report the state-of-the-art has been summarised. The aim of the project is to: Propose concepts and methods that improve the annual and short-term timetable planning. Demonstrate how the proposed timetable planning concepts improve the prerequisites for real-time network management. Develop methods and tools that can reduce inefficiencies in real time network management. An important aspect is to improve the coordination between yards/terminals and the line network, and between Infrastructure Manager, Yard Managers, and freight Rail Undertakings. We motivate our research by the current situation in Sweden, which is characterised by low on-time performance for freight trains, dense and heterogenous traffic on the major railway lines, and a rigid annual timetabling process, which is non-suitable for short-term changes. We believe that better tools for network planning and management on tactical and operational level can help to connect planning and operational processes. Aiming for improvements of the operational traffic, there is a need for systematic development of methods applied at several planning horizons, based on both simulation and optimization techniques. Close to operation fast methods are needed, for example, based on meta-heuristics. The maintenance planning process and improvement potential have been described. This is a new piece of the puzzle and it is important to close the gap between timetable planning and operational traffic. The different planning processes at the Infrastructure Manager, the Rail Undertakings and the Maintenance Contractors should be aligned. When developing new approaches for computational decision-support tools for real-time network management, it is important — but very challenging — to evaluate and benchmark with existing software tools. We also observe that the research stream on computational decision-support and algorithm development for railway traffic management has not yet been sufficiently merged with the corresponding research stream focusing on aspects of human computer interaction.Project lead: Magnus Wahlborg Editor: Anders PetersonShift2Rail/Fr8Rail I

fr8hub WP3 Deliverable 3.3 Results of traffic simulation of defined scenarios and evaluation (Shift2Rail)

### The deliverable is available online here: ### Link 1: https://cordis.europa.eu/project/id/777402 ### Link 2: https://projects.shift2rail.org/s2r_ip5_n.aspx?p=FR8HUB ### Abstract: The main aspects of the Shift2Rail project fr8hub are to increase efficiency at the nodes, hubs and terminals in the freight railway system. In addition, the impact of increasing freight train speeds on line capacity will be investigated. Furthermore, the development of freight locomotives of the future will be continued. WP3 delivers a demonstrator showcasing the effects of 1) improved traffic management through better interaction between line and yard, and 2) increased freight speed and its effects on overall increased capacity, punctuality and reduced travel time for both passenger and freight trains. To evaluate and validate the effects, two important lines on the Scandinavian-Mediterranean Corridor in the TEN-T network (Malmö - Hallsberg and Karlsruhe - Basel) in Sweden and Germany are used as case studies. ### fr8hub, Real-time information applications and energy efficient solutions for rail freight; project funded from the European Union's Horizon 2020 research and innovation programme (IP5 - freight