A Novel Approach for Big Data Classification and Transportation in Rail Networks

Saki, Mahdi; Lipman, Justin

doi:10.1109/tits.2019.2905611

Cited by 33 publications

(15 citation statements)

References 40 publications

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“…In Saki et al [ 378 ], the authors suggested a discontinuance of onboard data computing with the use of signal processing coupled with enriched machine-learning-based approaches. The authors’ reason for such a decision was to limit the transmission bandwidth on the public mobile networks.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

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Condition monitoring of rail transport systems has become a phenomenon of global interest over the past half a century. The approaches to condition monitoring of various rail transport systems—especially in the context of rail vehicle subsystem and track subsystem monitoring—have been evolving, and have become equally significant and challenging. The evolution of the approaches applied to rail systems’ condition monitoring has followed manual maintenance, through methods connected to the application of sensors, up to the currently discussed methods and techniques focused on the mutual use of automation, data processing, and exchange. The aim of this paper is to provide an essential overview of the academic research on the condition monitoring of rail transport systems. This paper reviews existing literature in order to present an up-to-date, content-based analysis based on a coupled methodology consisting of bibliometric performance analysis and systematic literature review. This combination of literature review approaches allows the authors to focus on the identification of the most influential contributors to the advances in research in the analyzed area of interest, and the most influential and prominent researchers, journals, and papers. These findings have led the authors to specify research trends related to the analyzed area, and additionally identify future research agendas in the investigation from engineering perspectives.

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Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

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“…This paper mainly deals with the relevant units and aspects that directly affect traffic management. For example, the police patrol, public security, and other parts share defense data, such as multifleet and hazardous chemical operation data, for example, the peak period of pedestrian and vehicle flow in the market, the time of school and holiday, as well as the number of students and details of traffic violation fines collected by banks [15].…”

Section: Application Of Big Data In Intelligent Transportationmentioning

confidence: 99%

The Application of Virtual Reality Technology on Intelligent Traffic Construction and Decision Support in Smart Cities

Yan

Chen

2021

Wireless Communications and Mobile Computing

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The core of smart city is to build intelligent transportation system.. An intelligent transportation system can analyze the traffic data with time and space characteristics in the city and acquire rich and valuable knowledge, and it is of great significance to realize intelligent traffic scheduling and urban planning. This article specifically introduces the extensive application of urban transportation infrastructure data in the construction and development of smart cities. This article first explains the related concepts of big data and intelligent transportation systems and uses big data to illustrate the operation of intelligent transportation systems in the construction of smart cities. Based on the machine learning and deep learning method, this paper is aimed at the passenger flow and traffic flow in the smart city transportation system. This paper deeply excavates the time, space, and other hidden features. In this paper, the traffic volume of the random sections in the city is predicted by using the graph convolutional neural network (GCNN) model, and the data are compared with the other five models (VAR, FNN, GCGRU, STGCN, and DGCNN). The experimental results show that compared with the other 4 models, the GCNN model has an increase of 8% to 10% accuracy and 15% fault tolerance. In forecasting morning and evening peak traffic flow, the accuracy of the GCNN model is higher than that of other models, and its trend is basically consistent with the actual traffic volume, the predicted results can reflect the actual traffic flow data well. Aimed at the application of intelligent transportation in an intelligent city, this paper proposes a machine learning prediction model based on big data, and this is of great significance for studying the mechanical learning of such problems. Therefore, the research of this paper has a good implementation prospect and academic value.

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“…It passed the final review in January 2020, marking that China railway had made important achievements in top level planning and design of intelligent high-speed railway. At the same time, global scholars have deeply researched the intelligent fields of train control [4], [5], train dispatch [6], [7], train communication [8], [9], railway traffic conflict control [10] and other fields [11], [12].…”

Section: Related Workmentioning

confidence: 99%

“…The main embodiment of intelligence in high-speed railway was the integration of advanced technology methods represented by machine learning [13], [14] and high-speed railway networks. Intelligent failure diagnosis and prediction [5], [15], [16], condition monitoring and health management [17] were studied, such as failure intelligent diagnosis of rolling bearings [18], [19] or bogies [20], [21] of high-speed trains, switch failure prediction [22], [23], vehicle-body vibration prediction [24], as well as online condition monitoring of the pantograph slide plate [25]. It can be seen that machine learning methods have achieved remarkable results compared with the traditional methods.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Failure Features of High-Speed Automatic Train Protection System

Kang

Wang

Chen³

et al. 2021

IEEE Access

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An automatic train protection (ATP) system is the core to ensure operation safety of high-speed railway. However, at present, failure rate change rules of the system are not well understood and the maintenance strategy is not refined. In order to improve the protection capability and maintenance level of high-speed trains, this paper proposes a decision tree machine learning model for failure feature extraction of ATP systems. First, system type, mean operation mileage, mean service time, etc. are selected as ATP failure feature parameters, and cumulative failure rate as a model output label. Second, support vector machine, AdaBoost, artificial neural networks and decision tree model are adopted to train and test practical failure data. Performance analysis shows that decision tree learning model has better generalization ability. Its accuracy of 0.9761 is significantly greater than the other machine learning models, so it is most suitable for failure features analysis. Third, interpretability analysis reveals the quantitative relationship between system failure and features. Finally, an intelligent maintenance system for ATP systems is built, which realize the refined maintenance throughout life cycle.INDEX TERMS Automatic train protection system, intelligent maintenance, failure feature, machine learning, model interpretability, high-speed train.

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A Novel Approach for Big Data Classification and Transportation in Rail Networks

Cited by 33 publications

References 40 publications

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

The Application of Virtual Reality Technology on Intelligent Traffic Construction and Decision Support in Smart Cities

Analysis of Failure Features of High-Speed Automatic Train Protection System

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