Paper Info
Title
A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors.
Abstract
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.
Year
DOI
Venue
2019
10.1155/2019/9162163
COMPLEXITY
Field
DocType
Volume
Truncation,Exponential function,Traffic flow,Tensor,Control theory,Algorithm,Predictive modelling,Multi-mode optical fiber,Intelligent transportation system,Periodic graph (geometry),Mathematics
Journal
2019
ISSN
Citations 
PageRank 
1076-2787
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Huiming Duan133.11
Xinping Xiao2204.78