Railway wagon flow routing locus pattern intelligent recognition algorithm based on SST
Purpose – The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Emerald Publishing
2020-04-01
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| Series: | Smart and Resilient Transportation |
| Subjects: | |
| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/SRT-10-2019-0008/full/pdf?title=railway-wagon-flow-routing-locus-pattern-intelligent-recognition-algorithm-based-on-sst |
| _version_ | 1797995620672208896 |
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| author | Xiaodong Zhang Ping Li Xiaoning Ma Yanjun Liu |
| author_facet | Xiaodong Zhang Ping Li Xiaoning Ma Yanjun Liu |
| author_sort | Xiaodong Zhang |
| collection | DOAJ |
| description | Purpose – The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm. Design/methodology/approach – Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized. Findings – This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm. Practical implications – Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast. Originality/value – As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization. |
| first_indexed | 2024-04-11T10:04:33Z |
| format | Article |
| id | doaj.art-f22609fad9f6444280476dcc7d64902c |
| institution | Directory Open Access Journal |
| issn | 2632-0495 |
| language | English |
| last_indexed | 2024-04-11T10:04:33Z |
| publishDate | 2020-04-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Smart and Resilient Transportation |
| spelling | doaj.art-f22609fad9f6444280476dcc7d64902c2022-12-22T04:30:17ZengEmerald PublishingSmart and Resilient Transportation2632-04952020-04-012132110.1108/SRT-10-2019-0008642813Railway wagon flow routing locus pattern intelligent recognition algorithm based on SSTXiaodong Zhang0Ping Li1Xiaoning Ma2Yanjun Liu3Research and Application Innovation Center for Big Data Technology in Railway, China Academy of Railway Sciences, Beijing, ChinaInstitute of Computing Technology, China Academy of Railway Sciences, Beijing, ChinaResearch and Application Innovation Center for Big Data Technology in Railway, China Academy of Railway Sciences, Beijing, ChinaResearch and Application Innovation Center for Big Data Technology in Railway, China Academy of Railway Sciences, Beijing, ChinaPurpose – The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm. Design/methodology/approach – Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized. Findings – This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm. Practical implications – Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast. Originality/value – As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.https://www.emerald.com/insight/content/doi/10.1108/SRT-10-2019-0008/full/pdf?title=railway-wagon-flow-routing-locus-pattern-intelligent-recognition-algorithm-based-on-sstintelligent transportationpattern recognitionsimhash algorithmwagon flow routingsimilarity matrixclustering analysis |
| spellingShingle | Xiaodong Zhang Ping Li Xiaoning Ma Yanjun Liu Railway wagon flow routing locus pattern intelligent recognition algorithm based on SST Smart and Resilient Transportation intelligent transportation pattern recognition simhash algorithm wagon flow routing similarity matrix clustering analysis |
| title | Railway wagon flow routing locus pattern intelligent recognition algorithm based on SST |
| title_full | Railway wagon flow routing locus pattern intelligent recognition algorithm based on SST |
| title_fullStr | Railway wagon flow routing locus pattern intelligent recognition algorithm based on SST |
| title_full_unstemmed | Railway wagon flow routing locus pattern intelligent recognition algorithm based on SST |
| title_short | Railway wagon flow routing locus pattern intelligent recognition algorithm based on SST |
| title_sort | railway wagon flow routing locus pattern intelligent recognition algorithm based on sst |
| topic | intelligent transportation pattern recognition simhash algorithm wagon flow routing similarity matrix clustering analysis |
| url | https://www.emerald.com/insight/content/doi/10.1108/SRT-10-2019-0008/full/pdf?title=railway-wagon-flow-routing-locus-pattern-intelligent-recognition-algorithm-based-on-sst |
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