A Multisource Data Fusion Modeling Prediction Method for Operation Environment of High-Speed Train
Providing accurate and reliable railway regional environmental data is a key consideration in operation control and dynamic dispatching of high-speed train. However, there are problems of low reliability and high uncertainty in the single data processing of high-speed train operating area environmen...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/5604783 |
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| _version_ | 1850221704243052544 |
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| author | Decang Li Juhui Zhang Ruxun Xu Jianjun Meng Jianming Wang Xiaoqiang Chen Xin Jia Junhui Ma |
| author_facet | Decang Li Juhui Zhang Ruxun Xu Jianjun Meng Jianming Wang Xiaoqiang Chen Xin Jia Junhui Ma |
| author_sort | Decang Li |
| collection | DOAJ |
| description | Providing accurate and reliable railway regional environmental data is a key consideration in operation control and dynamic dispatching of high-speed train. However, there are problems of low reliability and high uncertainty in the single data processing of high-speed train operating area environment. Therefore, this paper proposes a novel multisource sensor data fusion method based on a three-level information fusion framework. Firstly, the feature of the same kind of sensor data is extracted by the Kalman Filter (KF) algorithm as the input of back propagation neural network (BPNN). Then input the sample site into the BPNN for training and recognition, the feature fusion of heterogeneous sensor data is carried out, the decision output of BPNN is obtained, the output results are normalized, and its output is used as the basic probability assignment of Dempster–Shafer (D-S) evidence theory and synthesis rules. Finally, the decision fusion of multisource data is realized by D-S evidence theory. The simulation results show that compared with the traditional single fusion algorithm, the algorithm improves the accuracy of the prediction of high-speed train operation environment and reduces the MAPE from 13.82% to 7.455%, and the RMSE from 0.77 to 0.69, and meanwhile, increases the R2 from 0.87 to 0.97. |
| format | Article |
| id | doaj-art-64e206089a1a403ea23f6cdcf54db968 |
| institution | OA Journals |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-64e206089a1a403ea23f6cdcf54db9682025-08-20T02:06:39ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/5604783A Multisource Data Fusion Modeling Prediction Method for Operation Environment of High-Speed TrainDecang Li0Juhui Zhang1Ruxun Xu2Jianjun Meng3Jianming Wang4Xiaoqiang Chen5Xin Jia6Junhui Ma7Mechatronics T&R InstituteMechatronics T&R InstituteMechatronics T&R InstituteMechatronics T&R InstituteMechatronics T&R InstituteMechatronics T&R InstituteMechatronics T&R InstituteMechatronics T&R InstituteProviding accurate and reliable railway regional environmental data is a key consideration in operation control and dynamic dispatching of high-speed train. However, there are problems of low reliability and high uncertainty in the single data processing of high-speed train operating area environment. Therefore, this paper proposes a novel multisource sensor data fusion method based on a three-level information fusion framework. Firstly, the feature of the same kind of sensor data is extracted by the Kalman Filter (KF) algorithm as the input of back propagation neural network (BPNN). Then input the sample site into the BPNN for training and recognition, the feature fusion of heterogeneous sensor data is carried out, the decision output of BPNN is obtained, the output results are normalized, and its output is used as the basic probability assignment of Dempster–Shafer (D-S) evidence theory and synthesis rules. Finally, the decision fusion of multisource data is realized by D-S evidence theory. The simulation results show that compared with the traditional single fusion algorithm, the algorithm improves the accuracy of the prediction of high-speed train operation environment and reduces the MAPE from 13.82% to 7.455%, and the RMSE from 0.77 to 0.69, and meanwhile, increases the R2 from 0.87 to 0.97.http://dx.doi.org/10.1155/2022/5604783 |
| spellingShingle | Decang Li Juhui Zhang Ruxun Xu Jianjun Meng Jianming Wang Xiaoqiang Chen Xin Jia Junhui Ma A Multisource Data Fusion Modeling Prediction Method for Operation Environment of High-Speed Train Discrete Dynamics in Nature and Society |
| title | A Multisource Data Fusion Modeling Prediction Method for Operation Environment of High-Speed Train |
| title_full | A Multisource Data Fusion Modeling Prediction Method for Operation Environment of High-Speed Train |
| title_fullStr | A Multisource Data Fusion Modeling Prediction Method for Operation Environment of High-Speed Train |
| title_full_unstemmed | A Multisource Data Fusion Modeling Prediction Method for Operation Environment of High-Speed Train |
| title_short | A Multisource Data Fusion Modeling Prediction Method for Operation Environment of High-Speed Train |
| title_sort | multisource data fusion modeling prediction method for operation environment of high speed train |
| url | http://dx.doi.org/10.1155/2022/5604783 |
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