原文传递
Deterioration Prediction of Track Geometry Using Periodic Measurement Data and Incremental Support Vector Regression Model
| 题名： | Deterioration Prediction of Track Geometry Using Periodic Measurement Data and Incremental Support Vector Regression Model |
| 正文语种： | 英文 |
| 作者： | Jun S. Lee, Ph.D; Sung Ho Hwang, Ph.D.; II Yoon Choi, Ph.D.; and Yeongtae Choi, Ph.D. |
| 作者单位： | Research Fellow, Advanced Railroad Civil Engineering Div, Korea Railroad Research Institute;Advanced Railroad Civil Engineering Div, Korea Railroad Research Institute;Civil Engineering Div, Korea Railroad Research Institute;Civil Engineering Div, Korea Railroad Research Institute |
| 摘要： | Information on the quality of ballasted track is normally collected from a track measurement vehicle operating on a monthly basis or otherwise periodically. Track deterioration in terms of alignment and vertical levels is normally predicted by time-series data collected up to a certain point, and subsequent maintenance work is undertaken based on the predetermined maintenance level of the track geometry classified according to its importance. In this regard, deterioration of track geometry based on time-series measurement data can be efficiently modeled by an online support vector regression (OSVR) scheme, and detailed investigation has been carried out to improve the previous work on batch-type prediction models of track geometry proposed by the authors. For such purposes, an incremental support vector regression (ISVR) model based on a Bayesian optimization scheme as well as an OSVR model are introduced in this paper, and the prediction results are compared with those obtained by a conventional machine learning model. The results show that the accuracy of the proposed model increases by approximately 20% compared with that of the conventional model, and the outcome can be applied to the optimal scheduling of track maintenance work. |
| 出版日期： | 2020.01 |
| 出版年： | 2020 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | Vol.146 |
| 期： | No.01 |
| 页码： | 04019057 |
相关文献
- Prediction of Track Deterioration Using Maintenance Data and Machine Learning Schemes
- Traffic speed prediction for intelligent transportation system based on a deep feature fusion model
- Development of Railway Ride Comfort Prediction Model: Incorporating Track Geometry and Rolling Stock Conditions
- Spatial-Temporal Model to Identify the Deformation of Underlying High-Speed Railway Infrastructure
- Advancing Railway Asset Management Using Track Geometry Deterioration Modeling Visualization
- Displacement Model for Concrete Dam Safety Monitoring via Gaussian Process Regression Considering Extreme Air Temperature
检索历史
应用推荐
