Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning

Train station parking (TSP) accuracy is important to enhance the efficiency of train operation and the safety of passengers for urban rail transit. However, TSP is always subject to a series of uncertain factors such as extreme weather and uncertain conditions of rail track resistances. To increase the parking accuracy, robustness, and self-learning ability, we propose new train station parking frameworks by using the reinforcement learning (RL) theory combined with the information of balises. Three algorithms were developed, involving a stochastic optimal selection algorithm (SOSA), a Q-learning algorithm (QLA), and a fuzzy function based Q-learning algorithm (FQLA) in order to reduce the parking error in urban rail transit. Meanwhile, five braking rates are adopted as the action vector of the three algorithms and some statistical indices are developed to evaluate parking errors. Simulation results based on real-world data show that the parking errors of the three algorithms are all within the "mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"""mml:mrow""mml:mo"±"/mml:mo""/mml:mrow""/mml:math"30cm, which meet the requirement of urban rail transit. Document type: Articl

Position manipulation attacks to balise-based train automatic stop control

Singapore National Research Foundatio

A review on technologies for localisation and navigation in autonomous railway maintenance systems

Smart maintenance is essential to achieving a safe and reliable railway, but traditional maintenance deployment is costly and heavily human-involved. Ineffective job execution or failure in preventive maintenance can lead to railway service disruption and unsafe operations. The deployment of robotic and autonomous systems was proposed to conduct these maintenance tasks with higher accuracy and reliability. In order for these systems to be capable of detecting rail flaws along millions of mileages they must register their location with higher accuracy. A prerequisite of an autonomous vehicle is its possessing a high degree of accuracy in terms of its positional awareness. This paper first reviews the importance and demands of preventive maintenance in railway networks and the related techniques. Furthermore, this paper investigates the strategies, techniques, architecture, and references used by different systems to resolve the location along the railway network. Additionally, this paper discusses the advantages and applicability of on-board-based and infrastructure-based sensing, respectively. Finally, this paper analyses the uncertainties which contribute to a vehicle’s position error and influence on positioning accuracy and reliability with corresponding technique solutions. This study therefore provides an overall direction for the development of further autonomous track-based system designs and methods to deal with the challenges faced in the railway network.European Union’s Horizon 2020 research and innovation programme. Shift2Rail Joint Undertaking (JU): 88157

Rapid Algorithm for Generating and Selecting Optimal Metro Train Speed Curves Based on Alpha Zero and Expert Experience

According to the current research status of urban rail transit’s fully automatic operation (FAO), the train driving speed curves are usually obtained through simulation and calculation. The train driving speed curves obtained by this method not only have low efficiency but also are not suitable for complex road conditions. Inspired by AlphaZero, a reinforcement learning algorithm that utilised vast amounts of artificial data to defeat AlphaGo, an AI Go program, this paper investigates and analyses methods for rapidly generating a large number of speed curves and selecting those with superior performance for train operation. Firstly, we use the powerful third-party library in Python as the basis, combined with the idea of AlphaZero, to produce artificial speed curves for metro train driving. Secondly, we set relevant parameters with reference to expert experience to quickly produce massive reasonable artificial speed curves. Thirdly, we analysed relevant indicators such as energy consumption, running time error and passenger comfort to select some speed curves with better comprehensive performance. Finally, through the many observations with different running distances and different speed limits, we found that the speed curves produced and selected by our algorithm are more productive, diverse and conducive to the research of train driving operation than the actual data from traditional manual driving and ATO (automatic train operation) system

Entwicklung und Analyse eines Zug-zentrischen Entfernungsmesssystems mittels Colored Petri Nets

Based on the technology trends, the train control system should weaken the proportion of ground facilities, and give trains more individual initiative than in the past. As a result, the safety and flexibility of the train control system can be further improved. In this thesis, an enhanced movement authority system is proposed, which combines advantages of the train-centric communication with current movement authority mechanisms. To obtain the necessary train distance interval data, the onboard equipment and a new train-to-train distance measurement system (TTDMS) are applied as normal and backup strategies, respectively. While different location technologies have been used to collect data for trains, the development and validation of new systems remain challenges. In this thesis, formal approaches are presented for developing and verifying TTDMS. To assist the system development, the Colored Petri nets (CPNs) are used to formalize and evaluate the system structure and its behavior. Based on the CPN model, the system structure is validated. Additionally, a procedure is proposed to generate a Code Architecture from the formal model. The system performance is assessed in detection range and accuracy. Therefore both mathematical simulation and practical measurements validation are implemented. The results indicate that the system is feasible to carry out distance measurements both in metropolitan and railway lines, and the formal approaches are reusable to develop and verify other systems. As the target object, TTDMS is based on a spread-spectrum technology to accomplish distance measurement. The measurement is carried out by applying Time of Arrival (TOA) to calculate the distance between two trains, and requires no synchronized time source of transmission. It can calculate the time difference by using the autocorrelation of Pseudo Random Noise (PRN) code. Different from existing systems in air and maritime transport, this system does not require any other localization unit, except for communication architecture. To guarantee a system can operate as designed, it needs to be validated before its application. Only when system behaviors have been validated other relative performances' evaluations make sense. Based on the unambiguous definition of formal methods, TTDMS can be described much clearer by using formal methods instead of executable codes.Basierend auf technologischen Trends sollte das Zugbeeinflussungssystem den Anteil der Bodenanlagen reduzieren und den Zügen mehr Eigeninitiative geben als in der Vergangenheit, da so die funktionale Sicherheit und die Flexibilität des Zugbeeinflussungssystems erhöht werden können. In dieser Arbeit wird ein verbessertes System vorgeschlagen, das die Vorteile der zugbezogenen Kommunikation mit den aktuellen Fahrbefehlsmechanismen kombiniert. Um die notwendigen Daten des Zugabstandsintervalls zu erhalten, werden die Bordausrüstung und ein neues Zug-zu-Zug-Entfernungsmesssystem (TTDMS) als normale bzw. Backup-Strategien angewendet. Während verschiedene Ortungstechnolgien zur Zugdatenerfassung genutzt wurden, bleibt die Entwicklung und Validierung neuer Systeme eine Herausforderung. In dieser Arbeit werden formale Ansätze zur Entwicklung und Verifikation von TTDMS vorgestellt. Zur Unterstützung der Systementwicklung werden CPNs zur Formalisierung und Bewertung der Systemstruktur und ihres Verhaltens eingesetzt. Basierend auf dem CPN-Modell wird die Systemstruktur validiert. Zusätzlich wird eine Methode vorgeschlagen, mit der eine Code-Architektur aus dem formalen Modell generiert werden kann. Die Systemleistung wird im Erfassungsbereich und in der Genauigkeit beurteilt. Daher werden sowohl eine mathematische Simulation als auch eine praktische Validierung der Messungen implementiert. Die Ergebnisse zeigen, dass das System in der Lage ist, Entfernungsmessungen in Metro- und Eisenbahnlinien durchzuführen. Zudem sind die formalen Ansätze bei der Entwicklung und Verifikation anderer Systeme wiederverwendbar. Die Abstandsmessung mit TTDMS basiert auf einem Frequenzspreizungsverfahren. Die Messung wird durchgeführt, indem die Ankunftszeit angewendet wird, um den Abstand zwischen zwei Zügen zu berechnen. Dieses Verfahren erfordert keine Synchronisierung der Zeitquellen der Übertragung. Der Zeitunterschied kann damit berechnet werden, indem die Autokorrelation des Pseudo-Random-Noise-Codes verwendet wird. Im Unterschied zu Systemen im Luft- und Seeverkehr benötigt dieses System keine andere Lokalisierungseinheit als die Kommunikationsarchitektur. Um zu gewährleisten, dass ein System wie vorgesehen funktioniert, muss es validiert werden. Nur wenn das Systemverhalten validiert wurde, sind Bewertungen anderer relativer Leistungen sinnvoll. Aufgrund ihrer eindeutigen Definition kann das TTDMS mit formalen Methoden klarer beschrieben werden als mit ausführbaren Codes