Impact of signalling system on capacity – Comparing legacy ATC, ETCS level 2 and ETCS hybrid level 3 systems

Ranjbar, Vahid; Olsson, Nils O.E.; Sipilä, Hans

doi:10.1016/j.jrtpm.2022.100322

Cited by 7 publications

(4 citation statements)

References 21 publications

(30 reference statements)

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“…Pascoe and Eichorn introduced the concept of CBTC and how it works, and the CBTC system was compared with other traditional train control systems from the structures, functions and working principles [1]. Ranjbar, et al represented the characters of European Train Control System Level 2 (ETCS L2), hybrid level 3 (ETCS HL3), and analyzed the impacts of typical signaling systems on transport capacity [2]. In the RBC-TCS such as Chinese Train Control System -Level 3 (CTCS-3), train movements pursuing a speed of 350 km/h are controlled by the movement authorities (MAs), which are generated by RBCs.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Parallel simulation of high-speed trains controlled by radio block centers using Spark cloud

Xin

Zhou

Mei³

et al. 2022

Preprint

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Train movement prediction simulation is an effective method of enhancing operation safety and efficiency of railway transportation. In reality, train movement data generated by the trains running in a railway network are huge, and on the other hand, the data quantity generated from data processing is also explosively increased for the analysis and decision such as conflict recognition and scheduling optimization, which greatly increases the time cost of prediction simulation. This paper attempts to propose a train movement model driven by the movement authorities (MAs) issued from radio block centers (RBCs) and its parallel simulation algorithm realized on Spark cloud. This paper provides a solution of iterative computing of dynamic process simulation based on cloud. Different from the general big data processing of independent datasets, the resilient distributed datasets are expandable along iterative computing processes. The Dataframe of SparkSQL modules on Apache Spark is employed to handle the problems of usage interdependency of datasets. Each worker node independently and synchronously deals with the data belonging to a part of trains, and at the same time, requires the data from other worker nodes belonging to other parts of trains. The parallel simulation is realized by Scala language that is used to build the Spark platform. The simulation results on a high-speed railway network demonstrates that the proposed train movement model and parallel algorithm can achieve theoretical rationality and decrease the time cost to satisfy real-time performance.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Parallel simulation of high-speed trains controlled by radio block centers using Spark cloud

Xin

Zhou

Mei³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The simulation conditions include railway network conditions, train conditions, and cloud computing Divide DF_Train into J partitions DF_Train [1], DF_Train [2], ., and DF_Train[ J], and send each partition to a worker node. 9:…”

Section: Simulation Conditionsmentioning

confidence: 99%

“…Pascoe and Eichorn 1 introduced the concept of CBTC and how it works, and the CBTC system was compared with other traditional train control systems from the structures, functions, and working principles. Ranjbar et al 2 represented the characters of European Train Control System Level 2 (ETCS L2), hybrid level 3 (ETCS HL3), and analyzed the impacts of typical signaling systems on transport capacity. In the RBC-TCS such as Chinese Train Control System – Level 3 (CTCS-3), train movements pursuing a speed of 350 km/h are controlled by the movement authorities (MAs), which are generated by RBCs.…”

Section: Introductionmentioning

confidence: 99%

Parallel simulation of high-speed trains controlled by radio block centers using Spark cloud

Tao

Zhou

Mei

et al. 2023

Advances in Mechanical Engineering

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Train movement prediction simulation is an effective method of enhancing operation safety and efficiency of railway transportation. Train movement data generated by the trains running in a railway network are huge, and on the other hand, the data quantity generated from data processing is also explosively increased for the analysis and decision such as conflict recognition and scheduling optimization, which greatly increases the time cost of prediction simulation. This paper attempts to propose a train movement model driven by the movement authorities (MAs) issued from radio block centers (RBCs) and its parallel simulation algorithm realized on Spark cloud. This paper provides a solution of iterative computing of dynamic process simulation based on cloud. Different from the general big data processing of independent datasets, the resilient distributed datasets are expandable along iterative computing processes. The Dataframe of SparkSQL modules on Apache Spark is employed to handle the problems of usage interdependency of datasets. The parallel simulation is realized by Scala language that is used to build the Spark platform. The simulation results on a high-speed railway network demonstrates that the proposed train movement model and parallel algorithm can achieve theoretical rationality and decrease the time cost to satisfy real-time performance.

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“…The Train Control System (TCS) plays an important role in the safety assurance of train operation and has been the key to fostering railway transportation system competitiveness. Great effort has been made in finalizing the technical standards of TCS, including the European Train Control System (ETCS) and the Chinese Train Control System (CTCS) [1,2]. In modern TCSs, the increasing autonomy of the train's on-board system indicates a higher requirement to the capability in the full lifecycle, and there is a growing demand for system health monitoring and early assessment.…”

Section: Introductionmentioning

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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Impact of signalling system on capacity – Comparing legacy ATC, ETCS level 2 and ETCS hybrid level 3 systems

Cited by 7 publications

References 21 publications

WITHDRAWN: Parallel simulation of high-speed trains controlled by radio block centers using Spark cloud

WITHDRAWN: Parallel simulation of high-speed trains controlled by radio block centers using Spark cloud

Parallel simulation of high-speed trains controlled by radio block centers using Spark cloud

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

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