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A hybrid algorithm for estimating origin-destination flows

Li, Xianghua, Kurths, Jürgen, Gao, Chao, Zhang, Junwei, Wang, Zhen and Zhang, Zili 2017, A hybrid algorithm for estimating origin-destination flows, IEEE access, vol. 6, pp. 677-687, doi: 10.1109/ACCESS.2017.2774449.

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Title A hybrid algorithm for estimating origin-destination flows
Author(s) Li, Xianghua
Kurths, Jürgen
Gao, Chao
Zhang, Junwei
Wang, Zhen
Zhang, ZiliORCID iD for Zhang, Zili orcid.org/0000-0002-8721-9333
Journal name IEEE access
Volume number 6
Start page 677
End page 687
Total pages 11
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2017-11-16
ISSN 2169-3536
Keyword(s) predictive models
prediction algorithms
autoregressive processes
public transportation
Kalman filters
principal component analysis
neural networks
science & technology
technology
computer science, information systems
engineering, electrical & electronic
telecommunications
computer science
engineering
origin-destination matrix
nonnegative matrix factorization
autoregressive model
Summary With the development of intelligent transportation systems, the estimation of traffic flow in urban areas has attracted a great attention of researchers. The timely and accurate travel information of urban residents could assist users in planning their travel strategies and improve the operational efficiency of intelligent transportation systems. Currently, the origin-destination (OD) flows of urban residents are formulated as an OD matrix, which is used to denote the travel patterns of urban residents. In this paper, a simple and effective model, called NMF-AR, is proposed for predicting the OD matrices through combining the nonnegative matrix factorization (NMF) algorithm and the Autoregressive (AR) model. The basic characteristics of travel flows are first revealed based on the NMF algorithm. Then, the nonlinear time series coefficient matrix, extracted from the NMF algorithm, is estimated based on the AR model. Finally, we predict OD matrices based on the estimated coefficient matrix and the basis matrix of NMF. Extensive experiments have been implemented, in collected real data about taxi GPS information in Beijing, for comparing our proposed algorithm with some known methods, such as different kinds of $K$-nearest neighbor algorithms, neural network algorithms and classification algorithms. The results show that our proposed NMF-AR algorithm have a more effective capability in predicting OD matrices than other models.
Language eng
DOI 10.1109/ACCESS.2017.2774449
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30107022

Document type: Journal Article
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.