zhang-hybridalgorithm-2018.pdf (2.61 MB)
A hybrid algorithm for estimating origin-destination flows
journal contribution
posted on 2017-11-15, 00:00 authored by X Li, J Kurths, C Gao, J Zhang, Z Wang, Zili ZhangZili ZhangWith 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.
History
Journal
IEEE accessVolume
6Pagination
677 - 687Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
Link to full text
eISSN
2169-3536Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2017, IEEEUsage metrics
Keywords
predictive modelsprediction algorithmsautoregressive processespublic transportationKalman filtersprincipal component analysisneural networksScience & TechnologyTechnologyComputer Science, Information SystemsEngineering, Electrical & ElectronicTelecommunicationsComputer ScienceEngineeringOrigin-destination matrixnonnegative matrix factorizationautoregressive modelGPSpredictionTRAFFIC VOLUMEArtificial Intelligence and Image Processing
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