A Train Protection Logic Based on Topological Manifolds for Virtual Coupling

Zhang, Yong; Wang, Haifeng; James, Phillip; Roggenbach, Markus

doi:10.1109/tits.2021.3108840

Cited by 16 publications

(4 citation statements)

References 27 publications

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“…Proving the correctness of a train control system with formal methods is an important research direction [27]. Runtime verification is a lightweight formal method that can predict undesired behaviours while the system is running [28].…”

Section: B Predictions Of Train Operationsmentioning

confidence: 99%

A Relative Operation-Based Separation Model for Safe Distances of Virtually Coupled Trains

Chai

Wang

Tang

et al. 2024

IEEE Trans. Intell. Veh.

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Virtual coupling is a novel railway transport concept that allows trains to split and join on-the-fly by switching from mechanical to virtual couplers. One of the main challenges in applying virtual coupling in metro railways is to reduce the tracking distance between trains without compromising safety. This paper proposes a relative operation-based train separation model to reduce the safe distance between trains. This model applies a fault tolerance principle. The principle is that the preceding train normally operates for a time interval from its last-known state before initiating an emergency brake to stop the train. A difficulty in applying the proposed model is to predict the boundary of all possible time-position trajectories of the preceding train, which is the reachability problem of a hybrid system. To solve this problem, we formalise the operation of the preceding train by a parameterized hybrid automaton. A polytope-based algorithm is then developed for computing an over-approximated reachable set of the automaton. We compare our approach with a state-of-the-art relative braking distance-based train separation model for virtual coupling on a concrete metro line in Chengdu, China, and evaluate the method with several benchmarks. The results demonstrate that the relative operation-based model substantially reduces the safe distances between trains. Compared to conventional approaches, the proposed model provides a considerable 90.7% decrease in unnecessary waiting time at railway stations for virtually coupled trains and a 4.9% increase in the capacity of the given railway lines.

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Section: B Predictions Of Train Operationsmentioning

confidence: 99%

A Relative Operation-Based Separation Model for Safe Distances of Virtually Coupled Trains

Chai

Wang

Tang

et al. 2024

IEEE Trans. Intell. Veh.

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“…In China, the CTCS framework was established to define the standards of the signaling systems. According to the specifications, CTCS is divided into five levels [28]. Taking CTCS level 2 as an example, it consists of the digital track circuits (or analog track circuits with multi-information), transponders (Balise), and the on-board equipment with an Automatic Train Protection (ATP) function.…”

Section: System Architecture and Fault Modes Of On-board Tcsmentioning

confidence: 99%

Fault Prediction of On-Board Train Control Equipment Using a CGAN-Enhanced XGBoost Method with Unbalanced Samples

Liu

Cai

et al. 2023

Machines

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On-board train control equipment is an important component of the Train Control System (TCS) of railway trains. In order to guarantee the safe and efficient operation of the railway system, Predictive Maintenance (PdM) is significantly required. The operation data of the on-board equipment allow us to build fault prediction models using a data-driven approach. However, the problem of unbalanced fault samples makes it difficult to achieve the expected modeling performance. In this paper, a Conditional Generative Adversarial Network (CGAN) is adopted to solve the unbalancing problem by generating synthetic samples corresponding to specific fault labels that belong to the minority classes. With this basis, a CGAN-enhanced eXtreme Gradient Boosting (XGBoost) solution is presented for training the fault prediction models. From the pre-processing to the field data, artificial fault samples are generated and integrated into the training sample sets, and the XGBoost models can be derived with multiple decision trees. Both the feature importance sequence list and the knowledge graph are derived to describe the characteristics obtained by the models. Filed data sets from practical operation are utilized to validate the proposed solution. By comparison with conventional machine learning algorithms, it can be found that higher accuracy, precision, recall, and F1 scores, which are up to 99.76%, can be achieved by the proposed solution. By involving the CGAN strategy, the maximum enhancement to the F1 score with the XGBoost approach reaches 6.13%. The advantages of the proposed solution show great potential in implementing equipment health management and intelligent condition-based maintenance.

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“…Finally, reference [129] proposed a general modeling method based on topological manifolds to describe the protection logic for VCTS. Furthermore, reference [130] proposed a calculation model for station spacing to minimize the passenger travel time for increasing the station spacing and passenger transport efficiency.…”

Section: Optimization and Optimal Controlmentioning

confidence: 99%

Virtual Coupling in Railways: A Comprehensive Review

Félez

Vaquero-Serrano

2023

Machines

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The current mobility situation is constantly changing as people are increasingly moving to urban areas. Therefore, a flexible mode of transport with high-capacity passenger trains and a high degree of modularity in the trains’ composition is necessary. Virtual coupling (VC) is a promising solution to this problem because it significantly increases the capacity of a line and provides a more flexible mode of operation than conventional signaling systems. This novel review, in which approximately 200 papers were analyzed, identifies the main topics of current railway-related VC research, and represents the first step toward the implementation of VC in future railways. It was found that industry research has mainly focused on the feasibility of VC implementation and operation, whereas in academia, which is coordinated with industry, research has focused on control and communication systems. From a technological perspective, the main challenges for VC were identified with regard to aspects such as safety, control technology, interlocking, vehicle-to-vehicle communication, cooperative train protection and control, and integrated traffic management. The important directions for future research that have been identified for future development include complete dynamic models, real-time controllers, reliable and secure communication, different communication topologies, cybersecurity, intelligent control, reinforcement learning, and Big Data analytics.

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A Train Protection Logic Based on Topological Manifolds for Virtual Coupling

Cited by 16 publications

References 27 publications

A Relative Operation-Based Separation Model for Safe Distances of Virtually Coupled Trains

A Relative Operation-Based Separation Model for Safe Distances of Virtually Coupled Trains

Fault Prediction of On-Board Train Control Equipment Using a CGAN-Enhanced XGBoost Method with Unbalanced Samples

Virtual Coupling in Railways: A Comprehensive Review

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