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

Infrastructure Design, Signalling and Security in Railway

Railway transportation has become one of the main technological advances of our society. Since the first railway used to carry coal from a mine in Shropshire (England, 1600), a lot of efforts have been made to improve this transportation concept. One of its milestones was the invention and development of the steam locomotive, but commercial rail travels became practical two hundred years later. From these first attempts, railway infrastructures, signalling and security have evolved and become more complex than those performed in its earlier stages. This book will provide readers a comprehensive technical guide, covering these topics and presenting a brief overview of selected railway systems in the world. The objective of the book is to serve as a valuable reference for students, educators, scientists, faculty members, researchers, and engineers

Ondas milimétricas e MIMO massivo para otimização da capacidade e cobertura de redes heterogeneas de 5G

Today's Long Term Evolution Advanced (LTE-A) networks cannot support the exponential growth in mobile traffic forecast for the next decade. By 2020, according to Ericsson, 6 billion mobile subscribers worldwide are projected to generate 46 exabytes of mobile data traffic monthly from 24 billion connected devices, smartphones and short-range Internet of Things (IoT) devices being the key prosumers. In response, 5G networks are foreseen to markedly outperform legacy 4G systems. Triggered by the International Telecommunication Union (ITU) under the IMT-2020 network initiative, 5G will support three broad categories of use cases: enhanced mobile broadband (eMBB) for multi-Gbps data rate applications; ultra-reliable and low latency communications (URLLC) for critical scenarios; and massive machine type communications (mMTC) for massive connectivity. Among the several technology enablers being explored for 5G, millimeter-wave (mmWave) communication, massive MIMO antenna arrays and ultra-dense small cell networks (UDNs) feature as the dominant technologies. These technologies in synergy are anticipated to provide the 1000_ capacity increase for 5G networks (relative to 4G) through the combined impact of large additional bandwidth, spectral efficiency (SE) enhancement and high frequency reuse, respectively. However, although these technologies can pave the way towards gigabit wireless, there are still several challenges to solve in terms of how we can fully harness the available bandwidth efficiently through appropriate beamforming and channel modeling approaches. In this thesis, we investigate the system performance enhancements realizable with mmWave massive MIMO in 5G UDN and cellular infrastructure-to-everything (C-I2X) application scenarios involving pedestrian and vehicular users. As a critical component of the system-level simulation approach adopted in this thesis, we implemented 3D channel models for the accurate characterization of the wireless channels in these scenarios and for realistic performance evaluation. To address the hardware cost, complexity and power consumption of the massive MIMO architectures, we propose a novel generalized framework for hybrid beamforming (HBF) array structures. The generalized model reveals the opportunities that can be harnessed with the overlapped subarray structures for a balanced trade-o_ between SE and energy efficiently (EE) of 5G networks. The key results in this investigation show that mmWave massive MIMO can deliver multi-Gbps rates for 5G whilst maintaining energy-efficient operation of the network.As redes LTE-A atuais não são capazes de suportar o crescimento exponencial de tráfego que está previsto para a próxima década. De acordo com a previsão da Ericsson, espera-se que em 2020, a nível global, 6 mil milhões de subscritores venham a gerar mensalmente 46 exa bytes de tráfego de dados a partir de 24 mil milhões de dispositivos ligados à rede móvel, sendo os telefones inteligentes e dispositivos IoT de curto alcance os principais responsáveis por tal nível de tráfego. Em resposta a esta exigência, espera-se que as redes de 5a geração (5G) tenham um desempenho substancialmente superior às redes de 4a geração (4G) atuais. Desencadeado pelo UIT (União Internacional das Telecomunicações) no âmbito da iniciativa IMT-2020, o 5G irá suportar três grandes tipos de utilizações: banda larga móvel capaz de suportar aplicações com débitos na ordem de vários Gbps; comunicações de baixa latência e alta fiabilidade indispensáveis em cenários de emergência; comunicações massivas máquina-a-máquina para conectividade generalizada. Entre as várias tecnologias capacitadoras que estão a ser exploradas pelo 5G, as comunicações através de ondas milimétricas, os agregados MIMO massivo e as redes celulares ultradensas (RUD) apresentam-se como sendo as tecnologias fundamentais. Antecipa-se que o conjunto destas tecnologias venha a fornecer às redes 5G um aumento de capacidade de 1000x através da utilização de maiores larguras de banda, melhoria da eficiência espectral, e elevada reutilização de frequências respetivamente. Embora estas tecnologias possam abrir caminho para as redes sem fios com débitos na ordem dos gigabits, existem ainda vários desafios que têm que ser resolvidos para que seja possível aproveitar totalmente a largura de banda disponível de maneira eficiente utilizando abordagens de formatação de feixe e de modelação de canal adequadas. Nesta tese investigamos a melhoria de desempenho do sistema conseguida através da utilização de ondas milimétricas e agregados MIMO massivo em cenários de redes celulares ultradensas de 5a geração e em cenários 'infraestrutura celular-para-qualquer coisa' (do inglês: cellular infrastructure-to-everything) envolvendo utilizadores pedestres e veiculares. Como um componente fundamental das simulações de sistema utilizadas nesta tese é o canal de propagação, implementamos modelos de canal tridimensional (3D) para caracterizar de forma precisa o canal de propagação nestes cenários e assim conseguir uma avaliação de desempenho mais condizente com a realidade. Para resolver os problemas associados ao custo do equipamento, complexidade e consumo de energia das arquiteturas MIMO massivo, propomos um modelo inovador de agregados com formatação de feixe híbrida. Este modelo genérico revela as oportunidades que podem ser aproveitadas através da sobreposição de sub-agregados no sentido de obter um compromisso equilibrado entre eficiência espectral (ES) e eficiência energética (EE) nas redes 5G. Os principais resultados desta investigação mostram que a utilização conjunta de ondas milimétricas e de agregados MIMO massivo possibilita a obtenção, em simultâneo, de taxas de transmissão na ordem de vários Gbps e a operação de rede de forma energeticamente eficiente.Programa Doutoral em Telecomunicaçõe

Bayesian Beamforming for Mobile Millimeter Wave Channel Tracking in the Presence of DOA Uncertainty

This paper proposes a Bayesian approach for angle-based hybrid beamforming and tracking that is robust to uncertain or erroneous direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Because the resolution of the phase shifters is finite and typically adjustable through a digital control, the DOA can be modeled as a discrete random variable with a prior distribution defined over a discrete set of candidate DOAs, and the variance of this distribution can be introduced to describe the level of uncertainty. The estimation problem of DOA is thereby formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. To alleviate the computational complexity and cost, we present a motion trajectory-constrained a priori probability approximation method. It suggests that within a specific spatial region, a directional estimate can be close to true DOA with a high probability and sufficient to ensure trustworthiness. We show that the proposed approach has the advantage of robustness to uncertain DOA, and the beam tracking problem can be solved by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art benchmarks.This work was in part supported by the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2020ZT012), Beijing Jiaotong University and China Railway Corporation (Contract No. N2019G028). This article was presented in part at the 2019 IEEE GLOBECOM’19. The associate editor coordinating the review of this article and approving it for publication was O. Oyman. (Corresponding author: Yan Yang.) Yan Yang is with the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, Chin

Channel estimation and beam training with machine learning applications for millimetre-wave communication systems

The fifth generation (5G) wireless system will extend the capabilities of the fourth generation (4G) standards to serve more users and provide timely communication. To this end, the carriers of 5G systems will be able to operate at higher frequency bands, such as the millimetre-wave (mmWave) bands that span from 30 GHz to 300 GHz, to obtain greater bandwidths and higher data rates. As a result, the deployment of 5G networks is required to accommodate more antennas and offer pervasive coverage with controlled power consumption. The complexity of 5G systems introduces new challenges to traditional signal processing techniques. To address these challenges, a major step is to integrate machine learning (ML) algorithms into wireless communication systems. ML can learn patterns from datasets to achieve control and optimisation of complex radio frequency (RF) networks. This PhD thesis focuses on developing efficient channel estimation methods and beam training strategies with the application of ML algorithms for mmWave wireless systems. Firstly, the channel estimation and signal detection problem is investigated for orthogonal frequency-division multiplexing (OFDM) systems that operate at mmWave bands. A deep neural network (DNN)-based joint channel estimation and signal detection approach is proposed to achieve multi-user detection in a one-shot process for non-orthogonal multiple access (NOMA) systems. The DNN acts as the receiver, which can recover the transmitted data by learning the channel implicitly from suitable training. The proposed approach can be adapted to work for both single-input and single-output (SISO) systems and multiple-output and multipleoutput (MIMO) systems. This DNN-based approach is shown to provide good performance for OFDM systems that suffer from severe inter-symbol interference or where small numbers of pilot symbols are used. Secondly, the beam training and tracking problem is studied for mmWave channels with receiver mobility. To reduce the signalling overhead caused by frequent beam training, a lowcomplexity beam training strategy is proposed for mobile mmWave channels, which searches a set of selected beams obtained based on the recent beam search results. By searching only the adjacent beams to the one recently used, the proposed beam training strategy can reduce the beam training delay significantly while maintaining high transmission rates. The proposed strategy works effectively for channel datasets generated using either the stochastic or the raytracing channel model. This strategy is shown to approach the performance for an exhaustive beam search while saving up to 92% on the required beam training overhead. Thirdly, the proposed low-complexity beam training strategy is enhanced with the use of deep reinforcement learning (DRL) for mobile mmWave channels. A DRL-based beam training algorithm is proposed, which can intelligently switch between different beam training methods such that the average beam training overhead is minimised while achieving good spectral efficiency or energy efficiency performance. Given the desired performance requirement in the reward function for the DRL model, the spectral efficiency or energy efficiency can be maximised for the current channel condition by controlling the number of activated RF chains. The DRL-based approach can adjust the amount of beam training overhead required according to the dynamics of the environment. This approach can provide a good overhead-performance trade-off and achieve higher data rates in channels with significant levels of signal blockage

The influence of road safety culture on driver behaviour: a study of Nigerian drivers

Unsafe driver behaviour is regarded as the most significant contributory factor in road traffic crashes in Nigeria, and the prevailing road safety culture in the country is one aspect which sustains the high crash rate. This research used a problem-oriented approach with the intention to recommend research-based solutions to road safety problems in Nigeria while considering cultural and environmental factors that provoke different driving styles and behaviours. It aims to identify which, among culture and road environment, has a stronger influence on drivers’ behaviour and how behavioural changes can be achieved. To achieve this, a multi-method approach was adopted in different phases. Phase 1, an exploratory study involved on-road observation of traffic behaviour and conflicts in Nigeria using the Traffic Conflict Technique (TCT). It provided an understanding of the general traffic behaviour of various road users, showed the effect of various factors on conflict severity and helped to identify the most prevalent unsafe behaviours found in this environment. Based on the results of this study, a driving simulator experiment was designed and carried out in Phase 2, comparing the driving style of three groups of drivers in varying road conditions. These were Nigerians with no experience of driving in the United Kingdom (UK), Nigerians with some experience of driving in the UK and UK drivers. The conditions varied depending on how much regulation was provided (low or high infrastructure). A short road safety awareness-raising intervention for Nigerian drivers with no experience of driving in the UK was also evaluated. It was hypothesised that those Nigerian drivers with no experience of driving in a highly regulated UK road system would not be encouraged to adopt a safe driving style. This would have implications for the use of road safety interventions in Nigeria that have been developed outside the Nigerian context. In addition, participants completed the Driving Behaviour Questionnaire (DBQ) to compare reported behaviour and objectively measured driving behaviour in various traffic scenarios (overtaking, lane changing, car following etc.). Since many road safety measures could not evaluated for Nigerian drivers in phase 2, a focus group study was conducted in Phase 3 with the lead road safety agency in Nigeria-the Federal Road Safety Corps (FRSC). The study investigated the perceived effectiveness and ease of implementation of a wide range of road safety measures on drivers’ behaviour including those that were evaluated in Phase 2 (simple engineering measures and awareness-raising campaigns). Results provided a greater understanding of the road safety situation in Nigeria. Some of the unsafe behaviours identified in Phase 1 are distinct and can only be found in a particular cultural environment like Nigeria because of the traffic conditions and vehicle fleet. Investigating some of these behaviours in Phase 2 and comparing them with the behaviour of drivers from other cultures showed that there were distinct differences in behaviour between all the groups in most of the traffic scenarios. Nigerian drivers with no experience of driving in the UK were more likely to engage in unsafe driving behaviours compared to other groups. Improvements in the road environment did not bring about any significant changes in the behaviour of this group of drivers. However, small changes were observed after the awareness-raising intervention. The results indicate that the behaviour of drivers are interpretable in relation to their traffic safety culture, and are only partly influenced by their driving environment. Specifically, drivers’ traffic safety culture has a greater influence on their behaviour compared to changes in the road environment. Findings from the focus group study in phase 3 revealed that road safety measures such as education and information campaigns are perceived to have the potential to be very effective and easy to implement in Nigeria compared to other measures. The research findings provide an innovative approach to defining the key safety-critical behaviours which are prevalent in Nigeria as well as starting to understand how features of the road environment and/or training could be used to improve the road safety record in Nigeria. It also has implications for the design of road safety interventions in developing countries, particularly with respect to the non-portability of infrastructure measures from developing countries

Advances on Smart Cities and Smart Buildings

Modern cities are facing the challenge of combining competitiveness at the global city scale and sustainable urban development to become smart cities. A smart city is a high-tech, intensive and advanced city that connects people, information, and city elements using new technologies in order to create a sustainable, greener city; competitive and innovative commerce; and an increased quality of life. This Special Issue collects the recent advancements in smart cities and covers different topics and aspects

Light Weight Alloys: Processing, Properties and Their Applications

There is growing interest in light metallic alloys for a wide number of applications owing to their processing efficiency, processability, long service life, and environmental sustainability. Aluminum, magnesium, and titanium alloys are addressed in this Special Issue, however, the predominant role played by aluminum. The collection of papers published here covers a wide range of topics that generally characterize the performance of the alloys after manufacturing by conventional and innovative processing routes