4.7 Article

Nonlinear analysis of a new car-following model accounting for the optimal velocity changes with memory

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.cnsns.2016.04.024

关键词

Nonlinear analysis; Car-following model; Traffic flow; Stability

资金

  1. Hunan Provincial Natural Science Foundation of China [14JJ2125]
  2. Scientific Research Fund of Hunan Provincial Education Department, China [15A132]
  3. Second Phase of Doctor Scientific Research Startup Project Foundation of Hunan University of Arts and Science, China [14BSQD13]
  4. Key Project of the Scientific Research Foundation of Hunan University of Arts and Science, China [13ZD14]
  5. construction program of the key disciplines in Hunan University of Arts and Science, Strategic Research Grant, City University of Hong Kong [CityU-SRG 7004360]
  6. Natural Science Foundation of China [11302125]

向作者/读者索取更多资源

We, in this study, construct a new car-following model by accounting for the effect of the optimal velocity changes with memory in terms of the full velocity difference (FVD) model. The stability condition and mKdV equation concerning the optimal velocity changes with memory are derived through both linear stability and nonlinear analyses, respectively. Then, the space concerned can be divided into three regions classified as the stable, the metastable and the unstable ones. Moreover, it is shown that the effect of the optimal velocity changes with memory could enhance the stability of traffic flow. Furthermore, the numerical results verify that not only the sensitivity parameter of the optimal velocity changes with memory of driver but also the memory step could effectively stabilize the traffic flow. In addition, the stability of traffic flow is strengthened by increasing the memory step-size of optimal velocity changes and the intensity of drivers' memory with such changes. Most importantly, the effect of the optimal velocity changes with memory may avoid the disadvantage of historical information, which decreases the stability of traffic flow on road. (C) 2016 Elsevier B.V. All rights reserved.

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