A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors
Short-term traffic flow prediction is an important theoretical basis for intelligent transportation systems, and traffic flow data contain abundant multimode features and exhibit characteristic spatiotemporal correlations and dynamics. To predict the traffic flow state, it is necessary to design a m...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/9162163 |
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| _version_ | 1832568457079554048 |
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| author | Huiming Duan Xinping Xiao |
| author_facet | Huiming Duan Xinping Xiao |
| author_sort | Huiming Duan |
| collection | DOAJ |
| description | Short-term traffic flow prediction is an important theoretical basis for intelligent transportation systems, and traffic flow data contain abundant multimode features and exhibit characteristic spatiotemporal correlations and dynamics. To predict the traffic flow state, it is necessary to design a model that can adapt to changing traffic flow characteristics. Thus, a dynamic tensor rolling nonhomogeneous discrete grey model (DTRNDGM) is proposed. This model achieves rolling prediction by introducing a cycle truncation accumulated generating operation; furthermore, the proposed model is unbiased, and it can perfectly fit nonhomogeneous exponential sequences. In addition, based on the multimode characteristics of traffic flow data tensors and the relationship between the cycle truncation accumulated generating operation and matrix perturbation to determine the cycle of dynamic prediction, the proposed model compensates for the periodic verification of the RSDGM and SGM grey prediction models. Finally, traffic flow data from the main route of Shaoshan Road, Changsha, Hunan, China, are used as an example. The experimental results show that the simulation and prediction results of DTRNDGM are good. |
| format | Article |
| id | doaj-art-c2ac6f803a8f4cc6a411799f1da0e63c |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c2ac6f803a8f4cc6a411799f1da0e63c2025-02-03T00:59:03ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/91621639162163A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension TensorsHuiming Duan0Xinping Xiao1School of Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Science, Wuhan University of Technology, Wuhan 430070, ChinaShort-term traffic flow prediction is an important theoretical basis for intelligent transportation systems, and traffic flow data contain abundant multimode features and exhibit characteristic spatiotemporal correlations and dynamics. To predict the traffic flow state, it is necessary to design a model that can adapt to changing traffic flow characteristics. Thus, a dynamic tensor rolling nonhomogeneous discrete grey model (DTRNDGM) is proposed. This model achieves rolling prediction by introducing a cycle truncation accumulated generating operation; furthermore, the proposed model is unbiased, and it can perfectly fit nonhomogeneous exponential sequences. In addition, based on the multimode characteristics of traffic flow data tensors and the relationship between the cycle truncation accumulated generating operation and matrix perturbation to determine the cycle of dynamic prediction, the proposed model compensates for the periodic verification of the RSDGM and SGM grey prediction models. Finally, traffic flow data from the main route of Shaoshan Road, Changsha, Hunan, China, are used as an example. The experimental results show that the simulation and prediction results of DTRNDGM are good.http://dx.doi.org/10.1155/2019/9162163 |
| spellingShingle | Huiming Duan Xinping Xiao A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors Complexity |
| title | A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors |
| title_full | A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors |
| title_fullStr | A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors |
| title_full_unstemmed | A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors |
| title_short | A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors |
| title_sort | multimode dynamic short term traffic flow grey prediction model of high dimension tensors |
| url | http://dx.doi.org/10.1155/2019/9162163 |
| work_keys_str_mv | AT huimingduan amultimodedynamicshorttermtrafficflowgreypredictionmodelofhighdimensiontensors AT xinpingxiao amultimodedynamicshorttermtrafficflowgreypredictionmodelofhighdimensiontensors AT huimingduan multimodedynamicshorttermtrafficflowgreypredictionmodelofhighdimensiontensors AT xinpingxiao multimodedynamicshorttermtrafficflowgreypredictionmodelofhighdimensiontensors |