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

Using information engineering to understand the impact of train positioning uncertainties on railway subsystems

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. 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

Ant Colony Optimisation for Dynamic and Dynamic Multi-objective Railway Rescheduling Problems

Recovering the timetable after a delay is essential to the smooth and efficient operation of the railways for both passengers and railway operators. Most current railway rescheduling research concentrates on static problems where all delays are known about in advance. However, due to the unpredictable nature of the railway system, it is possible that further unforeseen incidents could occur while the trains are running to the new rescheduled timetable. This will change the problem, making it a dynamic problem that changes over time. The aim of this work is to investigate the application of ant colony optimisation (ACO) to dynamic and dynamic multiobjective railway rescheduling problems. ACO is a promising approach for dynamic combinatorial optimisation problems as its inbuilt mechanisms allow it to adapt to the new environment while retaining potentially useful information from the previous environment. In addition, ACO is able to handle multi-objective problems by the addition of multiple colonies and/or multiple pheromone and heuristic matrices. The contributions of this work are the development of a junction simulator to model unique dynamic and multi-objective railway rescheduling problems and an investigation into the application of ACO algorithms to solve those problems. A further contribution is the development of a unique two-colony ACO framework to solve the separate problems of platform reallocation and train resequencing at a UK railway station in dynamic delay scenarios. Results showed that ACO can be e ectively applied to the rescheduling of trains in both dynamic and dynamic multi-objective rescheduling problems. In the dynamic junction rescheduling problem ACO outperformed First Come First Served (FCFS), while in the dynamic multi-objective rescheduling problem ACO outperformed FCFS and Non-dominated Sorting Genetic Algorithm II (NSGA-II), a stateof- the-art multi-objective algorithm. When considering platform reallocation and rescheduling in dynamic environments, ACO outperformed Variable Neighbourhood Search (VNS), Tabu Search (TS) and running with no rescheduling algorithm. These results suggest that ACO shows promise for the rescheduling of trains in both dynamic and dynamic multi-objective environments.Engineering and Physical Sciences Research Council (EPSRC

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

Proceedings of the 3rd International Conference on Models and Technologies for Intelligent Transportation Systems 2013

Challenges arising from an increasing traffic demand, limited resource availability and growing quality expectations of the customers can only be met successfully, if each transport mode is regarded as an intelligent transportation system itself, but also as part of one intelligent transportation system with “intelligent” intramodal and intermodal interfaces. This topic is well reflected in the Third International Conference on “Models and Technologies for Intelligent Transportation Systems” which took place in Dresden 2013 (previous editions: Rome 2009, Leuven 2011). With its variety of traffic management problems that can be solved using similar methods and technologies, but with application specific models, objective functions and constraints the conference stands for an intensive exchange between theory and practice and the presentation of case studies for all transport modes and gives a discussion forum for control engineers, computer scientists, mathematicians and other researchers and practitioners. The present book comprises fifty short papers accepted for presentation at the Third Edition of the conference. All submissions have undergone intensive reviews by the organisers of the special sessions, the members of the scientific and technical advisory committees and further external experts in the field. Like the conference itself the proceedings are structured in twelve streams: the more model-oriented streams of Road-Bound Public Transport Management, Modelling and Control of Urban Traffic Flow, Railway Traffic Management in four different sessions, Air Traffic Management, Water Traffic and Traffic and Transit Assignment, as well as the technology-oriented streams of Floating Car Data, Localisation Technologies for Intelligent Transportation Systems and Image Processing in Transportation. With this broad range of topics this book will be of interest to a number of groups: ITS experts in research and industry, students of transport and control engineering, operations research and computer science. The case studies will also be of interest for transport operators and members of traffic administration

Modeling Uncertainty in Rail Freight Operations: Implications for Service Reliability

This thesis presents an operational simulation tool to evaluate different rail operational policies aimed at increasing service reliability in large-scale multi-carrier rail networks. Operational policies that improve shipment connection reliability at shunting yards, such as priority-based classification, train holding and train cancellation policies can be evaluated using the tool. To support operational decisions needed to implement priority-based classification, an optimization based framework is proposed. Operational policies to improve train schedule reliability, such as including slack time in timetables to handle minor delays, and rescheduling strategies to manage large delays can also be evaluated using the tool. For minor delays, analytical method for deterministic analysis of propagation of delays in train traffic networks is proposed and demonstrated on the Washington DC Metrorail Network. Rescheduling strategies required to manage large delays in multi-carrier rail networks are also discussed herein. A dynamic slot request mechanism is proposed, wherein each carrier requests slots for N blocks ahead, to model rescheduling requests of multiple carriers competing for the slots. The proposed simulation tool is applied on a European rail freight network, the REORIENT network, to evaluate the effect of variability in border crossing times, slack time in timetable design, different rescheduling policies and slot request size (N) on service reliability and average delay to the trains in the system