SCHOOL OF ENGINEERING INTELLIGENT REAL-TIME TRAIN RESCHEDULING MANAGEMENT FOR RAILWAY SYSTEM By LINSHA DAI A thesis submitted to the University of Birmingham for the degree of DOCTOR OF PHILOSOPHY 14푡ℎ June 2016 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT Demand for railway transport has grown within the last few years and continuous growth is projected in future years. Consequently, rail networks are increasingly being operated at the limits of their capacity and stability. The issue of managing a large and complex railway system with continuous traffic flows and mixed train services in a safe and punctual manner is very important, especially after disruptive events. A large number of recovery algorithms have been developed in recent years to support dispatchers in making decisions during railway operation. However, these algorithms do not have the ability to provide solutions to all situations which arise as delays develop. In the first part of this thesis an analysis method is introduced which allows the visualisation and measurement of the propagation of delays in the railway network. By categorising the resilience of a railway operation into three levels according to the system response and operational strategies required for absorbing delays, train re-ordering and re-timing strategies are applied at junctions to solve real-time train rescheduling problems after minor disruptions to maintain a robust system. A simple case study has been conducted using the HERMES railway simulator in which different junction control strategies are applied in response to a delay situation. The case study shows how the visualisation and categorisation methods may be used to compare the effectiveness of different strategies in reducing the influence of knock-on delays. The BRaVE simulator and the University of Birmingham Single Train Simulator (STS) are also introduced and a train running estimation using STS is described. The rescheduling process of the simulation is illustrated. A practical single junction rescheduling problem is then defined and a number of representative rescheduling approaches are applied to solve the problem. The algorithms considered are: Timetable-Order-Enforce (TOE), First-Come-First-Served (FCFS), First-Leave-First-Served (FLFS), Brute Force (BF), Dynamic Programming (DP), Decision Tree Based Elimination (DTBE), Tabu Search (TS), Local Search (LS), Simulated Annealing (SA), Genetic Algorithms (GA) and Ant Colony Optimisation Algorithm (ACO). These approaches are investigated and tested on a series of delay scenarios in microscopic simulation, and rescheduling solutions are compared and analysed. A case study investigates how different levels of delays and numbers of constraints may affect the performance of algorithms for network-wide rescheduling in terms of quality of solution and computation time. A recommendation for using these approaches is given based on their performance on different delay scenarios, and this can be used as a reference for further local rescheduling in decision centres. In order to deal with operational dynamics, a methodology using performance-based supervisory control is proposed to provide rescheduling decisions over a wider area through the application of different rescheduling strategies in appropriate sequences. A single junction case study is designed to demonstrate how this process is realised, and it shows the improvement in reducing the propagation of delays by applying alternating approaches. Finally, an architecture for a real-time train rescheduling framework, based on the distributed artificial intelligence system, is designed in order to handle railway traffic in a large-scale network intelligently, with different decision centres (DCs) processing together. A case study based on part of the East Coast Main Line using real-world data is considered, which demonstrates the effectiveness of adopting supervisory control to provide the rescheduling options for individual DCs. Keywords: Real-time rescheduling, delay propagation, algorithms, supervisory control, DAI system. ACKNOWLEDGEMENTS I am very grateful to my supervisors, Professor Clive Roberts and Professor Felix Schmid, for their patient, continuous and attentive support and guidance during my study in the UK. I have greatly benefited from their inspiration, knowledge and experience in work and life. Without their generous support and encouragement, my study in the UK would not have been possible. I would also like to extend my gratitude to the following people at the Birmingham Centre of Railway Research and Education: Dr Dave Kirkwood for sharing his expertise on JAVA programming and train simulations, Dr Gemma Nicolson for her helpful suggestions and inspiration for this thesis and Miss Katherine Slater for her great help for proof reading on my academic writing. Many thanks also to all the members of the Birmingham Centre for Railway Research and Education for their kind help and supporting. As well, I would like to thank my husband, my father and my parents-in-law, for their great love and support, which is my most precious thing in life. Finally, I would like to say thank you to my mother, who was a great and selfless woman, for always loving me, supporting me, encouraging me and inspiring me, not only in the past, but also in the future. TABLE OF CONTENTS CHAPTER 1 INTRODCTION .................................................................................................................... 1 1.1 Research background ................................................................................................... 1 1.2 Motivation and objective ............................................................................................. 3 1.3 Outline of the thesis ..................................................................................................... 5 CHAPTER 2 AN OVERVIEW OF RECOVERY MODELS FOR REAL-TIME TRAIN RESCHEDULING ................................................................................................................ 7 2.1 Introduction of the train rescheduling problem ........................................................... 7 2.1.1 Classification of train rescheduling problem ........................................................... 8 2.1.2 Different types of train delays .................................................................................. 9 2.2 Problem description ................................................................................................... 10 2.3 Recovery models for real-time train rescheduling ..................................................... 12 2.3.1 Microscopic models ............................................................................................... 12 2.3.2 Macroscopic models ............................................................................................... 20 2.4 Conclusion ................................................................................................................. 26 CHAPTER 3 METHODOLOGY OF DELAY PROPAGATION VISUALISATION ........................ 28 3.1 Introduction to railway system resilience .................................................................. 28 3.1.1 Views on general terms .......................................................................................... 28 3.1.2 Resilience classification and definition .................................................................. 32 3.1.3 Influence factors ..................................................................................................... 33 3.2 Existing delay propagation models ............................................................................ 35 3.2.1 Analytical models ................................................................................................... 35 3.2.2 Simulation-based models ....................................................................................... 36 3.3 Delay propagation visualisation ................................................................................. 37 3.3.1 Introduction to the visualisation method ................................................................ 37 3.3.2 Key performance indicator [91] ............................................................................. 39 3.3.3 Visualisation examples ........................................................................................... 40 3.4 Case study of delay propagation with junction controls ............................................ 43 3.4.1 Introduction to algorithms at junctions .................................................................. 43 3.4.2 Design of experiments ............................................................................................ 44 3.4.3 Analysis of Results ................................................................................................. 50 3.4.4 Summary ...............................................................................................................