Adaptive Railway Traffic Control using Approximate Dynamic Programming

Railway networks around the world have become challenging to operate in recent decades, with a mixture of track layouts running several different classes of trains with varying operational speeds. This complexity has come about as a result of the sustained increase in passenger numbers where in many countries railways are now more popular than ever before as means of commuting to cities. To address operational challenges, governments and railway undertakings are encouraging development of intelligent and digital transport systems to regulate and optimise train operations in real-time to increase capacity and customer satisfaction by improved usage of existing railway infrastructure. Accordingly, this thesis presents an adaptive railway traffic control system for realtime operations based on a data-based approximate dynamic programming (ADP) approach with integrated reinforcement learning (RL). By assessing requirements and opportunities, the controller aims to reduce delays resulting from trains that entered a control area behind schedule by re-scheduling control plans in real-time at critical locations in a timely manner. The present data-based approach depends on an approximation to the value function of dynamic programming after optimisation from a specified state, which is estimated dynamically from operational experience using RL techniques. By using this approximation, ADP avoids extensive explicit evaluation of performance and so reduces the computational burden substantially. In this thesis, formulations of the approximation function and variants of the RL learning techniques used to estimate it are explored. Evaluation of this controller shows considerable improvements in delays by comparison with current industry practices

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

Dynamic Programming and Bayesian Inference

Dynamic programming and Bayesian inference have been both intensively and extensively developed during recent years. Because of these developments, interest in dynamic programming and Bayesian inference and their applications has greatly increased at all mathematical levels. The purpose of this book is to provide some applications of Bayesian optimization and dynamic programming

Optimizing speed profiles for sustainable train operation with wayside energy storage systems

Large hauling capability and low rolling resistance has put rail transit at the forefront of mass transportation mode sustainability in terms of congestion mitigation and energy conservation. As such, rail vehicles are one of the least energy-intensive modes of transportation and least environmentally polluting. Despite, these positives, improper driving habits and wastage of the braking energy through dissipation in braking resistors result in unnecessary consumption, extra costs to the operator and increased atmospheric greenhouse gas emissions. This study presents an intelligent method for the optimization of the number and locations of wayside energy storage system (WESS) units that maximize the net benefits of the operation of a rail line. First, the optimized speed profiles with and without WESS is determined for a single alignment segment. Then, using the speed profiles obtained as an input, the number and locations of the WESS units that maximize the net benefit is determined for an entire rail line. The energy recovery methods used comprise optimal coasting, regenerative braking, and positioning of the energy storage devices to achieve maximum receptivity. Coasting saves energy by maintaining motion with propulsion disabled, but this increases the total travel time. Regenerative braking converts the kinetic energy of the train into electrical energy for the powering of subsequent acceleration cycles and although it does not affect travel time, it reduces the time available for coasting, indicative of a tradeoff. The study entails the design of a model that simulates the movement of the train over an existing alignment section while considering alignment topography, speed limits, and train schedule. Since on-time performance is the priority of railroad operations, the simulator instructs the driver to operate according to several motion regimes to optimize the energy consumption while maintaining schedule. The model consists of several time-varying inputs which add increased levels of complexity to the problem. This, in addition to its combinatorial nature, necessitates a heuristic algorithm to solve it, because traditional analytical solution methods are deficient. The optimization problem is solved by applying Genetic Algorithms (GA) because of their ability to search for a global solution in a complex multi-dimensional space. This strategy adds sustainability and reduces the carbon footprint of the operator. A case study is conducted on a single segment of a commuter rail line and yields a 34% energy reduction. The case study is extended to an entire line with multiple segments where the aim is to optimize the locations of wayside energy storage devices (WESS) for maximum economic benefit. It was found that out of the 10 alignment segments in the study, a maximized benefit of over $600,000 was achieved with WESS units installed on only three of those segments. The methods derived in this study can be used to generate speed profiles for planning purposes, to assist in recovery from service disruptions, to plan for infrastructural upgrades related to energy harvesting or to assist in the development of Driver Advisory Systems (DAS)

Optimal train control on various track alignments considering speed and schedule adherence constraints

The methodology discussed in this dissertation contributes to the field of transit operational control to reduce energy consumption. Due to the recent increase in gasoline cost, a significant number of travelers are shifting from highway modes to public transit, which also induces higher transit energy consumption expenses. This study presents an approach to optimize train motion regimes for various track alignments, which minimizes total energy consumption subject to allowable travel time, maximum operating speed, and maximum acceleration/deceleration rates. The research problem is structured into four cases which consist of the combinations of track alignments (e.g., single vertical alignment and mixed vertical alignment) and the variation of maximum operating speeds (e.g., constant and variable). The Simulated Annealing (SA) approach is employed to search for the optimal train control, called golden run . To accurately estimate energy consumption and travel time, a Train Performance Simulation (TPS) is developed, which replicates train movements determined by a set of dynamic variables (e,g., duration of acceleration and cruising, coasting position, braking position, etc.) as well as operational constraints (e.g., track alignment, speed limit, minimum travel time, etc.) The applicability of the developed methodology is demonstrated with geographic data of two real world rail line segments of The New Haven Line of the Metro North Railroad: Harrison to Rye Stations and East Norwalk to Westport Stations. The results of optimal solutions and sensitivity analyses are presented. The sensitivity analyses enable a transit operator to quantify the impact of the coasting position, travel time constraint, vertical dip of the track alignment, maximum operating speed, and the load and weight of the train to energy consumption. The developed models can assist future rail system with Automatic Train Control (ATC), Automatic Train Operation (ATO) and Positive Train Control (PTC), or conventional railroad systems to improve the planning and operation of signal systems. The optimal train speed profile derived in this study can be considered by the existing signal system for determining train operating speeds over a route

Control of AC/DC microgrids with renewables in the context of smart grids including ancillary services and electric mobility

Microgrids are a very good solution for current problems raised by the constant growth of load demand and high penetration of renewable energy sources, that results in grid modernization through “Smart-Grids” concept. The impact of distributed energy sources based on power electronics is an important concern for power systems, where natural frequency regulation for the system is hindered because of inertia reduction. In this context, Direct Current (DC) grids are considered a relevant solution, since the DC nature of power electronic devices bring technological and economical advantages compared to Alternative Current (AC). The thesis proposes the design and control of a hybrid AC/DC Microgrid to integrate different renewable sources, including solar power and braking energy recovery from trains, to energy storage systems as batteries and supercapacitors and to loads like electric vehicles or another grids (either AC or DC), for reliable operation and stability. The stabilization of the Microgrid buses’ voltages and the provision of ancillary services is assured by the proposed control strategy, where a rigorous stability study is made. A low-level distributed nonlinear controller, based on “System-of-Systems” approach is developed for proper operation of the whole Microgrid. A supercapacitor is applied to deal with transients, balancing the DC bus of the Microgrid and absorbing the energy injected by intermittent and possibly strong energy sources as energy recovery from the braking of trains and subways, while the battery realizes the power flow in long term. Dynamical feedback control based on singular perturbation analysis is developed for supercapacitor and train. A Lyapunov function is built considering the interconnected devices of the Microgrid to ensure the stability of the whole system. Simulations highlight the performance of the proposed control with parametric robustness tests and a comparison with traditional linear controller. The Virtual Synchronous Machine (VSM) approach is implemented in the Microgrid for power sharing and frequency stability improvement. An adaptive virtual inertia is proposed, then the inertia constant becomes a system’s state variable that can be designed to improve frequency stability and inertial support, where stability analysis is carried out. Therefore, the VSM is the link between DC and AC side of the Microgrid, regarding the available power in DC grid, applied for ancillary services in the AC Microgrid. Simulation results show the effectiveness of the proposed adaptive inertia, where a comparison with droop and standard control techniques is conducted.As Microrredes são uma ótima solução para os problemas atuais gerados pelo constante crescimento da demanda de carga e alta penetração de fontes de energia renováveis, que resulta na modernização da rede através do conceito “Smart-Grids”. O impacto das fontes de energia distribuídas baseados em eletrônica de potência é uma preocupação importante para o sistemas de potência, onde a regulação natural da frequência do sistema é prejudicada devido à redução da inércia. Nesse contexto, as redes de corrente contínua (CC) são consideradas um progresso, já que a natureza CC dos dispositivos eletrônicos traz vantagens tecnológicas e econômicas em comparação com a corrente alternada (CA). A tese propõe o controle de uma Microrrede híbrida CA/CC para integrar diferentes fontes renováveis, incluindo geração solar e frenagem regenerativa de trens, sistemas de armazenamento de energia como baterias e supercapacitores e cargas como veículos elétricos ou outras (CA ou CC) para confiabilidade da operação e estabilidade. A regulação das tensões dos barramentos da Microrrede e a prestação de serviços anciliares são garantidas pela estratégia de controle proposta, onde é realizado um rigoroso estudo de estabilidade. Um controlador não linear distribuído de baixo nível, baseado na abordagem “System-of-Systems”, é desenvolvido para a operação adequada de toda a rede elétrica. Um supercapacitor é aplicado para lidar com os transitórios, equilibrando o barramento CC da Microrrede, absorvendo a energia injetada por fontes de energia intermitentes e possivelmente fortes como recuperação de energia da frenagem de trens e metrôs, enquanto a bateria realiza o fluxo de potência a longo prazo. O controle por dynamical feedback baseado numa análise de singular perturbation é desenvolvido para o supercapacitor e o trem. Funções de Lyapunov são construídas considerando os dispositivos interconectados da Microrrede para garantir a estabilidade de todo o sistema. As simulações destacam o desempenho do controle proposto com testes de robustez paramétricos e uma comparação com o controlador linear tradicional. O esquema de máquina síncrona virtual (VSM) é implementado na Microrrede para compartilhamento de potência e melhoria da estabilidade de frequência. Então é proposto o uso de inércia virtual adaptativa, no qual a constante de inércia se torna variável de estado do sistema, projetada para melhorar a estabilidade da frequência e prover suporte inercial. Portanto, o VSM realiza a conexão entre lado CC e CA da Microrrede, onde a energia disponível na rede CC é usada para prestar serviços anciliares no lado CA da Microrrede. Os resultados da simulação mostram a eficácia da inércia adaptativa proposta, sendo realizada uma comparação entre o controle droop e outras técnicas de controle convencionais

Maximum risk reduction with a fixed budget in the railway industry

Decision-makers in safety-critical industries such as the railways are frequently faced with the complexity of selecting technological, procedural and operational solutions to minimise staff, passengers and third parties’ safety risks. In reality, the options for maximising risk reduction are limited by time and budget constraints as well as performance objectives. Maximising risk reduction is particularly necessary in the times of economic recession where critical services such as those on the UK rail network are not immune to budget cuts. This dilemma is further complicated by statutory frameworks stipulating ‘suitable and sufficient’ risk assessments and constraints such as ‘as low as reasonably practicable’. These significantly influence risk reduction option selection and influence their effective implementation. This thesis provides extensive research in this area and highlights the limitations of widely applied practices. These practices have limited significance on fundamental engineering principles and become impracticable when a constraint such as a fixed budget is applied – this is the current reality of UK rail network operations and risk management. This thesis identifies three main areas of weaknesses to achieving the desired objectives with current risk reduction methods as: Inaccurate, and unclear problem definition; Option evaluation and selection removed from implementation subsequently resulting in misrepresentation of risks and costs; Use of concepts and methods that are not based on fundamental engineering principles, not verifiable and with resultant sub-optimal solutions. Although not solely intended for a single industrial sector, this thesis focuses on guiding the railway risk decision-maker by providing clear categorisation of measures used on railways for risk reduction. This thesis establishes a novel understanding of risk reduction measures’ application limitations and respective strengths. This is achieved by applying ‘key generic engineering principles’ to measures employed for risk reduction. A comprehensive study of their preventive and protective capability in different configurations is presented. Subsequently, the fundamental understanding of risk reduction measures and their railway applications, the ‘cost-of-failure’ (CoF), ‘risk reduction readiness’ (RRR), ‘design-operationalprocedural-technical’ (DOPT) concepts are developed for rational and cost-effective risk reduction. These concepts are shown to be particularly relevant to cases where blind applications of economic and mathematical theories are misleading and detrimental to engineering risk management. The case for successfully implementing this framework for maximum risk reduction within a fixed budget is further strengthened by applying, for the first time in railway risk reduction applications, the dynamic programming technique based on practical railway examples

Aspekte der Verkehrstelematik – ausgewählte Veröffentlichungen 2012

Mit dem vorliegenden Band wird ein Überblick über die Forschungsarbeit der Professur Verkehrsleitsysteme und -prozessautomatisierung an der Fakultät Verkehrswissenschaften „Friedrich List“ der Technischen Universität Dresden des Jahres 2012 geboten. Zwölf ausgewählte Veröffentlichungen der Mitarbeiter, hauptsächlich von nationalen und internationalen Konferenzen wurden dafür zusammengestellt. Das breite, alle Landverkehrssysteme umfassende Forschungsgebiet des Lehrstuhls hat seine Schwerpunkte in den Bereichen energieoptimale Steuerung im Schienenverkehr und dem Verkehrsmanagement des Straßenverkehrs. Im Bereich der energieoptimalen Steuerung im Schienenverkehr werden die Aspekte Haltvermeidung und dynamische Fahrzeitenregelung beispielsweise im Artikel „Predictive Energy-Efficient Running Time Control for Metro Lines“ beschrieben. Der Beitrag „Pilotierung eines Assistenzsystems zur kraftstoffsparenden Fahrweise im SPNV“ fokussiert auf das Forschungs- und Anwendungsgebiet der Fahrerassistenz. Darüber hinaus sind auch die intermodalen Wechselwirkungen zwischen ÖPNV und MIV thematisiert. Die Ausführungen in „Cooperative Traffic Signals for Energy Efficient Driving in Tramway Systems“ verdeutlichen, wie mit Hilfe eines kooperativ arbeitenden Steuerungssystems für lichtsignalgesteuerte Verkehrsknoten diese Verkehrsmodi zum beiderseitigen Vorteil miteinander verknüpft werden können. Das Verkehrsmanagementsystem der Stadt Dresden VAMOS, das in “The Traffic Management System VAMOS - from Research to Regular Operation” vorgestellt wird, bildet die Grundlage für einen großen Teil der weiteren, hauptsächlich straßenverkehrsbezogenen Arbeiten. Innovative Ansätze zum Erfassen und Fusionieren relevanter Daten aus vorhandenen Detektoren und Erfahrungen aus dem Betrieb des Systems werden ergänzt durch die Präsentation weiterer Möglichkeiten zur Nutzung von Verkehrsdaten, wie beispielsweise im Beitrag „Microscopic Real-Time Simulation of Dresden Using VAMOS-Data“ dargestellt wird