TIMETABLE MANAGEMENT TECHNIQUE IN RAILWAY CAPACITY ANALYSIS: DEVELOPMENT OF THE HYBRID OPTIMIZATION OF TRAIN SCHEDULES (HOTS) MODEL

There are two general approaches to improve the capacity in a rail corridor, either by applying new capital infrastructure investment or by improving the operation of the rail services. Techniques to evaluate the railway operation include modeling and optimization through the use of commercial timetable management and rail simulation tools. However, only a few of the existing tools include complete features of timetable management techniques (e.g. timetable compression) are equipped with an optimization model for rescheduling and timetable improvement and this is especially true when it comes to the U.S. rail environment that prevalently uses unstructured operation practices. This dissertation explores an application of timetable (TT) management techniques (e.g. rescheduling and timetable compression techniques) in the U.S. rail environment and their effect on capacity utilization and level of service (LOS) parameters. There are many tools and simulation packages used for capacity analysis, by both European and the U.S. rail industry, but due to the differences in the operating philosophy and network characteristics of these two rail systems, European studies tend to use timetable-based simulation tools (e.g. RailSys, OpenTrack) while the non-timetable based tools (e.g. RTC) are commonly used in the U.S. (Chapter 1). This research study investigated potential benefits of using a “Hybrid Simulation” approach that would combine the advantages of both the U.S. and European tools. Two case studies (a single track and a multiple-track case study) were developed to test the hybrid simulation approach, and it was concluded that applying timetable management techniques (e.g. timetable compression technique) is promising when implemented in a single track corridor (Chapter 2), but it is only applicable for the multiple track corridors under directional operation pattern (Chapter 3). To address this, a new heuristic rescheduling and rerouting technique was developed as part of the research to convert a multiple track case study from non-directional operation pattern to a fully directional operation pattern (Chapter 4). The knowledge and skills of existing software, obtained during the development and testing of “Hybrid Simulation”, was used to develop an analytical rescheduling/optimization model called “Hybrid Optimization of Train Schedules” (HOTS) (Chapter 5). While the results of the “Hybrid simulation approach” are promising, the method was also time consuming and challenging, as all respective details and database of the given corridors had to be replicated in both simulation tools. The “HOTS Model” could provide the same functions and features of train rescheduling, but with much less efforts and challenges as in the hybrid simulation. The HOTS model works in conjunction with any commercial rail simulation software and it can reschedule an initial timetable (with or without conflict) to provide a “Conflict-Free” timetable based on user-defined criteria. The model is applicable to various types of rail operations, including single, double and multiple-track corridors, under both directional and nondirectional operation patterns. The capabilities of the HOTS model were tested for the two case studies developed in the research, and its outcomes were compared to those obtained from the commercial software. It was concluded that the HOTS model performed satisfactorily in each of the test scenarios and the model results either improved or maintained the initial timetable characteristics. The results are promising for the future development of the model, but limitations in the current model structure, such as station capacity limits, should be addressed to improve the potential of applying the model for industrial applications

Evaluation of the delay management potential on a macroscopic level

In general, macroscopic models in delay management allow for the optimization of large networks with reasonable computational effort. The main limitations here arise from the aggregated consideration of the infrastructure. In this paper, an evaluation of potential application of macroscopic models for delay management through a real case study is discussed. A macroscopic model is built by applying first a micro-macro transformation on a calibrated microscopic model, to provide an exact calculation of minimum running times and headways. On this macroscopic model, two disruption scenarios are analyzed and solved by using Event Activity Networks, to show the potential benefits and the limitations of delay management. The case study is based on a real railway infrastructure in Switzerland, and it is implemented in LinTim, an opensource software, which allows for an integrated development of both the macroscopic scenario and the delay management solutions

Microscopic simulation of decentralized dispatching strategies in railways

This paper analyzes the effectiveness of decentralized strategies for dispatching rolling stock and train drivers in a railway system. Such strategies give operators a robust alternative in case centralized control fails due to an abundance of infrastructure or rolling stock disruptions or information system malfunctions. We test the performance of four rolling stock and two driver dispatching strategies in a microscopic simulation. Our test case is a part of the Dutch railway network, containing eleven stations linked by four train lines. We find that with the decentralized dispatching strategies, target frequencies of the lines are approximately met and train services are highly regular without large delays. Especially strategies that allow rolling stock to switch between lines result in a high performance

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown

Evaluation of railway system performance under changing levels of automation using a simulation framework

Modern mainline railways are under constant pressure to meet the demands of higher capacity and to improve their punctuality. Railway system designers and operators are increasingly looking to use automation as tool to enable proactive strategies to optimise the timetable, improve the reliability of the infrastructure & rolling stock, to allow for a more dynamic command & control system which can respond to passenger demand and overall to linearize the response behaviour of the system under duress. In the first part of this thesis, I, the author, will discuss the development of automation over the years and the techniques that have been developed to analyse automation changes in a system. Further to this, I outline the various changes to the railway technology over the last century in brief. In the second part, I apply the techniques described earlier to design an automation framework to develop a grade of automation for the railway system to meet the demands of improved capacity and performance. Further to this, I develop parallel testable levels of automation using existing railway technology to demonstrate the effectiveness of a framework developed using the methodology discussed before. These levels are then tested on a network topology using micro-simulation to verify if they produce improved capacity and performance. In the final part, A case study is developed for the network from Kings Cross station to Welwyn Garden on the East Coast Main Line with the traffic dense branch line from Hertford north joining this line. The network is simulated under similar conditions to that adopted for the theoretical network and the results are compared with the previous outcomes. Results from the above studies have several significant outcomes. Firstly, the methodology developed over the course of this thesis can produce automation levels that are distinct from each other. Secondly, these simulation results show that there is a step change in the performance of the systems when organised into distinct levels of automation. Thirdly, and perhaps the most important conclusion from the studies, I show that automation of a single railway sub-system does not yield beneficial results unless there are complementary solutions produced for the surrounding sub-systems. In the theoretical phase of the study, the journey time calculations were repeated for 5000 iterations using a Quasi Monte Carlo framework. The results indicate a clear separation between each of the level and stages of automation proposed within the framework. The results from the simulation show that the reduction in journey times between the various levels can be as much as 5%. In the case study, the results were not as distinct but the overall trendlines indicate a reduction in journey times for both intercity and suburban services. Publications produced during the research period: • Venkateswaran, K., Nicholson, G., Chen, L. & Pelligrini, P. 2017. D3.3.2 Analysis of European best practices and levels of automation for traffic management under large disruptions In: IFFSTAR (ed.) Capacity for Rail. UIC. • Venkateswaran, K. G., Nicholson, G. L., Roberts, C. & Stone, R. Impact of Automation on the Capacity of a Mainline Railway: A Preliminary Hypothesis and Methodology. 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pages 2097-2102

Development of an upgrade selection process for railway renewal projects

Currently, many railway systems need to be upgraded to meet the demand for rapidly increasing railway capability, environmental concerns and customer satisfaction, while there is a lack of the right models and tools required to support the early decision making stage of railway renewal projects. In this thesis, a new railway selection upgrade process is proposed, which aims to support early stage decision-making in railway renewal projects by finding the most appropriate solutions to take forward for more detailed consideration. The railway selection upgrade process consists of modelling, simulation, split into macros-assessment and micro-simulation, and evaluation. A high-level feasibility analysis model is developed for the macro-assessment, to help engineers efficiently select the most promising upgrade options for further detailed consideration using microscopic simulation. This process provides a quick and efficient way to quantify evaluation functions, based on the 4Cs (capacity, carbon, customer satisfaction and cost) framework, to give a final suggestion on the most appropriate upgrade options. Two case studies, based on the East Coast Main Lines and the Northern Ireland railway network, are presented in order to demonstrate the application and verify the feasibility of the high-level feasibility analysis model and the railway upgrade selection process