Study of Railway Track Irregularity Standard Deviation Time Series Based on Data Mining and Linear Model

Abstract

Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation.

Track geometry degradation and maintenance modelling: A review

“…Li and Xiao, 17 Famurewa et al, 46 Liu et al, 57 Quiroga and Schnieder, 58 Chaolong et al 59 To get the benefit from updating the model parameters using new measurement data and to deal with small data Artificial neural network Guler 7 and Chaolong et al 59 To predict degradation by considering a large number of influencing factors Time series analysis Chaolong et al 56 To estimate the next degradation level using recent measurement data.…”

“…Chaolong et al. 56 applied time series analysis to predict track irregularity using standard deviation time series data. They identified different patterns and specifications of track irregularity behaviour using the clustering approach.…”

Efficient Processing of ContinuousSkylineQuery over Smarter Traffic Data Stream for Cloud Computing

“…Real-time transportation data stream lays important data foundation for road transportation stream control of various decision analysis and emergency response in smarter transportation system. Skyline [6] is of great significance in multiconstrained decision support, city navigation, user preference query, visualization of data mining, and so on under dynamic environment [7][8][9]. Hence, such query is consistent with practical application of data stream processing of smarter transportation.…”

Continuous Distributed Top-k Monitoring over High-Speed Rail Data Stream in Cloud Computing Environment

“…Real-time visibility is the basis of many advanced applications, which can enhance the railway network capacity, improve fuel efficiency, and asset utilization. This application needs regular measurement of various operating parameters associated with high-speed rail or passenger flow, so as to improve the overall operation of high-speed rail [9][10][11]. On the other hand, the application of fault detection is to monitor components of the high-speed rail (such as wheels and carriage) and avoid catastrophic events (such as derailment occurred).…”