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Bifurcation analysis of a new stochastic traffic flow model

  • WenHuan Ai EMAIL logo , RuiHong Tian , DaWei Liu und WenShan Duan
Veröffentlicht/Copyright: 13. Oktober 2022
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Abstract

The stochastic function describing the stochastic behavior of traffic flow in the process of acceleration or deceleration can better capture the stochastic characteristics of traffic flow. Based on this, we introduce the stochastic function into a high-order viscous continuous traffic flow model and propose a stochastic traffic flow model. Furthermore, we performed the bifurcation analysis of traffic flow system based on the model. Accordingly, the traffic flow problem is transformed into the stability analysis problem of the system, highlighting the unstable traffic characteristics such as congestion. The model can be used to study the nonlinear dynamic behavior of traffic flow. Based on this model, the existence of Hopf bifurcation and the saddle-node bifurcation is theoretically proved. And the type of the Hopf bifurcation is theoretically derived. The model can also be used to study the mutation behavior of system stability at bifurcation point. From the density space-time diagram of the system, we find that the system undergoes a stability mutation when it passes through the bifurcation point, which is consistent with the theoretical analysis results.


Corresponding author: WenHuan Ai, College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, 730070, China, E-mail:

Funding source: “Qizhi” Personnel Training Support Project of Lanzhou Institute of Technology

Award Identifier / Grant number: 2018QZ-11

Funding source: Natural Science Foundation of Gansu Province of China

Award Identifier / Grant number: No. 20JR5RA533

Funding source: China Postdoctoral Science Foundation Funded Project

Award Identifier / Grant number: No.: 2018M633653XB

Award Identifier / Grant number: No. 61863032

Funding source: Gansu Province Educational Research ProjectÂ

Award Identifier / Grant number: No. 2021A-166

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: The authors would like to thank the anonymous referees and the editor for their valuable opinions. This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61863032, 11965019) and the Natural Science Foundation of Gansu Province of China under the Grant No. 20JR5RA533 and the China Postdoctoral Science Foundation Funded Project (Project No: 2019M633653XB) and the “Qizhi” Personnel Training Support Project of Lanzhou Institute of Technology (2018QZ-11) and Gansu Province Educational Research Project (Grant No. 2021A-166).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2021-10-21
Accepted: 2022-09-18
Published Online: 2022-10-13

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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