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The Theoretical Research on Grey Catastrophe Model of Traffic System
Author(s) -
Huan Guo,
Yunxin Zhang,
Zhang Hongmao
Publication year - 2022
Publication title -
journal of mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.252
H-Index - 13
eISSN - 2314-4785
pISSN - 2314-4629
DOI - 10.1155/2022/4448294
Subject(s) - catastrophe theory , variable (mathematics) , traffic system , mathematics , construct (python library) , traffic generation model , state variable , function (biology) , state (computer science) , traffic congestion reconstruction with kerner's three phase theory , stability (learning theory) , traffic congestion , computer science , transport engineering , engineering , statistics , algorithm , mathematical analysis , physics , geotechnical engineering , evolutionary biology , machine learning , biology , thermodynamics , programming language
As we all know, it is difficult for traditional traffic models to deal with the phenomenon of “catastrophes” in traffic state and the traffic problem with incomplete information at the same time. Aiming at the traffic congestion, this paper firstly takes traffic volume as a state variable, speed, and density as control variables to establish the system potential function and theoretically clarifies that the structure of the traffic system has catastrophe characteristics. Secondly, combined with the grey characteristics of traffic system, we construct the grey catastrophe model for traffic system and obtain the traffic potential function with the system time as the state variable by substituting the variables of the model. Finally, the traffic stability is analyzed theoretically based on the potential function. In this paper, the rationality of the grey catastrophe model of traffic system is expounded theoretically, and the grey model theory is well expanded.

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