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Report on Traffic Flow Model D4.2 DESTination RAIL – Decision Support Tool for Rail Infrastructure Managers Project Reference: 636285 H2020-MG 2014-2015 Innovations and Networks Executive Agency Project Duration: 1 May 2015–31 April 2018 Report on Traffic Flow Model D4.2 Authors Jelena AKSENTIJEVIC Johann BLIEBERGER Mark STEFAN * Andreas SCHÖBEL *Corresponding author: Andreas Schöbel, [email protected] Date: May, 10th 2017 Dissemination level: (PU, PP, RE, CO): PU This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 636285 D4.2 Report on Traffic Flow Model DESTination RAIL – Decision Support Tool for Rail Infrastructure Managers DOCUMENT HISTORY Number Date Author(s) Comments 1 20/03/2017 Andreas Schöbel First Draft 2 14/04/2017 Jelena Aksentijevic Introduction, Data Flow Model, Case Studies 3 25/04/2017 Mark Stefen, Jelena Kronecker Algebra Aksentijevic 4 1/05/2017 Andreas Schöbel Executive Summary 5 7/05/2017 Mark Stefan Case studies 6 8/05/2017 Andreas Schöbel Summary and Conclusions 7 9/05/2017 Jelena Aksentijevic Final proofreading 8 23/06/2017 Vijay Ramdas Final Review 9 10/07/2017 Ken Gavin, Julie Clarke Final Version 2 D4.2 Report on Traffic Flow Model DESTination RAIL – Decision Support Tool for Rail Infrastructure Managers Table of Contents Executive Summary4 1 Introduction5 2 Dataflow Model7 2.1 Input Part 1: Infrastructure, Rolling Stock and Timetable7 2.2 Input Part 2: Infrastructure Manager Assessment Request7 3.2.1 Assessment of consequences of restricted availability of infrastructure assets8 3.2.2 Assessment of consequences of operational incident8 3.2.3 Assessment of benefits from infrastructure enhancement8 4 Kronecker Algebra for Railway Operation10 4.1 Introduction into Kronecker Algebra based Railway System Optimization10 4.2 Physical train model11 4.3 Calculation of the optimal driving strategy of a train12 4.4 Kronecker Algebra for analysis and optimization of railway systems13 4.5 System optimisation17 5 Irish Rail Case Study25 5.1 Northern Line Malahide – Drogheda – Dundalk25 5.2 Dublin Area31 6 Summary and Conclusions52 References53 3 D4.2 Report on Traffic Flow Model DESTination RAIL – Decision Support Tool for Rail Infrastructure Managers Executive Summary During maintenance work on infrastructure assets, the management of railway traffic is a challenging task for modern railway operation. Of course, all assets in use have to fulfil high requirements in terms of availability. Nevertheless, they are expected to eventually fail and therefore will need to be maintained or replaced in a timely fashion (i.e. before they start having severe impacts on daily operations). Once sophisticated models are able to predict the right timing for replacement, traffic flow will have to be rescheduled. Typically, maintenance work is carried out during night time because typically there is significantly less traffic than during day time or even no traffic. Maintenance works may also be scheduled for off-peak periods. Unfortunately, some tasks may even require closure of sections between two cross overs for a longer period of time. If this happens on a single track line, passenger trains need to be replaced by bus service and cargo trains will be either rerouted or rescheduled. On a double track line, the capacity issue has to be taken into account. Depending on the distance between two neighbouring cross overs, capacity may be significantly reduced. This leads to the question of which trains can be cancelled or delayed. To address this, a number of constraints have to be considered. This task is usually managed manually or with very little support from existing IT systems. The aim of Task 4.3 within the frame of Destination Rail Project was to develop a methodology to support Infrastructure Managers in their decision making process concerning traffic planning during maintenance work. A mathematical framework, called Kronecker Algebra, has been selected as a promising methodology, even for real time applications e.g. Driver Advisory Systems in accordance to the objectives in the description of work. While the performance of most algorithms decreases when additional constraints are added, Kronecker Algebra can deliver faster results with the addition of more constraints. This behaviour is very suitable for application in railway operations since we are dealing with a lot of constraints that need to be considered in finding solutions for real applications. Operational parameters like the infrastructure asset characteristics, the rolling stock and the original timetable are used for calculations under reduced availability of sections or track speed limits. Input data has been successfully collected from Irish Rail (Milestone 15). The output consists of optimised train runs in terms of punctuality and overall energy consumption and can be used for the evaluation of different scenarios for carrying out maintenance work for infrastructure assets. The traffic flow model has been successfully tested on the Northern Line between Malahide and Dundalk as well as on the Dublin Urban Area between Connolly and Pearse (Milestone 16). Ultimately however, it remains the responsibility of an infrastructure manager to decide which scenario is selected for real application. In comparison to existing practice, the infrastructure manager’s decision will be supported by more precise consequence data however. The developed traffic flow model allows a detailed assessment of the operational effects of any changes of network availability. 4 D4.2 Report on Traffic Flow Model DESTination RAIL – Decision Support Tool for Rail Infrastructure Managers 1 Introduction The current state-of-the art in terms of railway traffic operations consists of microscopic simulations of railway operations that are based upon physical and mathematical models of the railway system. Such tools generally provide output indicators in terms of operational performance; for example, delays and/or energy consumption. To date, optimisation has typically been predefined by the user of the tool, introduced into the simulation and ‘tested’ for its applicability through simulations. This has resulted in missed opportunities for finding optimal solutions, and has led to simulation programmes not being able to solve dispatching questions or handle headway conflicts. The traffic flow model developed in the Task 4.3 of Destination Rail includes innovative, graph-theory based mathematical techniques. Furthermore, it incorporates the ability to dynamically optimise operations, in particular degraded operations during maintenance or renewal works on the railway infrastructure. In addition to allowing a detailed assessment of the operational effects of any changes to network availability, this traffic flow model also uses new techniques to identify options for improving response to interruptions. In other words, this model incorporates the ability to mitigate the impacts of existing bottlenecks in the railway system by the rescheduling of trains whose performance could be affected. To put it differently, this model is capable of providing options to improve traffic flow as well as assessing the impact of maintenance and renewal strategies. This is done by optimising traffic flow while taking into account a number of different parameters, including energy consumption and punctuality. As a result, the model will allow an adaptation of the outputs to meet the specific requirements of different end-users. The traffic flow model has a modular structure and can therefore interface with the other tools developed and used in this project (including the Network Whole Life Cost Model developed in Task 4.4). Adopting an open data exchange standard for a software product entails many advantages like compatibility with other software components, accessible documentation, portability and flexibility. RailML 1 is a common open, xml-based exchange format used in the railway sector and it has been used as standard data exchange for the work package at hand. The standard comprises most of the input information needed to compute traffic flow optimization, divided into three schemata: infrastructure, rolling stock and timetable. Due to the number of different interfaces, this model can also contribute to improvements in Driver Advisory Systems (DAS) by providing reliable, ‘online’ movement authority optimisation to resolve conflicts at junctions arising from any source of disruption. The EU transport policy is focused on providing support for increasing the usage, the useable capacity and the performance of existing rail networks, for example by facilitating the switching of freight transport from road to rail through the prioritisation of the renewal and optimisation of new rail sections in order to reduce bottlenecks. The objective of the traffic flow model is to offer infrastructure managers a ‘plug and play’ tool for dealing with traffic modelling and optimisation functionalities that can help them test and compare different potential scenarios of network disturbances. Furthermore, by modelling their operational impacts, the model also has the capability to demonstrate other innovations developed in the project. 5 D4.2 Report on Traffic Flow Model DESTination RAIL – Decision Support Tool for Rail Infrastructure Managers Dataflow model offers an overview of data necessary for the application of the optimisation algorithm. The input data consists of two categories: 1) data for the simulation of the operational railway traffic flow (this includes
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