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Neural Computing and Applications

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27.07.2019 | Original Article

An inertia grey discrete model and its application in short-term traffic flow prediction and state determination

verfasst von: Huiming Duan, Xinping Xiao, Qinzi Xiao

Erschienen in: Neural Computing and Applications | Ausgabe 12/2020

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Abstract

A traffic flow system is a complex dynamic system. Traffic flows data are the product of the velocity and density, and its data have dynamic and fluctuation characteristics. Therefore, three new inertia grey discrete models (IDGMs) were proposed and used to estimate short-term traffic flow based on traffic flow data mechanics and characteristics and traffic-state characteristics. The modelling process of the traditional grey DGM using the least square method may lead to a large parameter estimation deviation and a low model precision. The new model uses the mechanical characteristics of the data and applies the evolutionary process of the mechanical decomposition of the data to the modelling process. It has a more reasonable modelling process and a more stable structure and solves the shortcomings of the traditional grey DGM parameter estimation. Moreover, it uses matrix analysis to study the important characteristics of the IDGM, and it simplifies the forms of the parameter model and structural model. Then, the traffic flow of the Whitemud Drive City Expressway in Canada is analysed empirically, and the effect of the new model and the judgment of three-phase traffic flow state are analysed.

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Titel: An inertia grey discrete model and its application in short-term traffic flow prediction and state determination
verfasst von: Huiming Duan
Xinping Xiao
Qinzi Xiao
Publikationsdatum: 27.07.2019
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 12/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04364-w

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