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Hybrid characteristics of heterogeneous traffic flow mixed with electric vehicles considering the amplitude of acceleration and deceleration

Author

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  • Zhang, Jiahe
  • Qian, Yongsheng
  • Zeng, Junwei
  • Wei, Xuting
  • Li, Haijun

Abstract

In order to explore the influence of electric vehicles on the traffic flow characteristics of traditional fuel vehicles, this paper proposes an improved cellular automata model considering the physical properties of vehicles, such as, the acceleration and deceleration amplitude, relative speed and safety distance etc. Based on the sensitivity analysis of the randomization probability, combined with the actual data, the model parameters are quantified to simulate the electric-fuel vehicle mixed traffic flow. Through numerical simulation, the results show that compared with NS (Nagel–Schreckenberg) model and VE(Velocity Effect) model, the improved model can reproduce rich traffic phenomena and is closer to the measured data. With the increase of electric vehicle permeability, the flow rate and speed are increased, the congestion near the critical density is reduced and the traffic safety is improved. At high density, the congestion shows a trend of decreasing first, then increasing and then decreasing again, and the safety is lower than that of homogeneous traffic flow. The speed fluctuation of fuel vehicles is enlarged, and the speed of the overall traffic flow shows an upward trend. In addition, it is found that the TIT curve can identify the state change of traffic flow, and electric vehicles in high-density mixed flow cause greater disturbance to the speed of traffic flow and reduces traffic safety. The results of this paper can provide a basis for further research of electric-fuel vehicle hybrid traffic flow.

Suggested Citation

  • Zhang, Jiahe & Qian, Yongsheng & Zeng, Junwei & Wei, Xuting & Li, Haijun, 2023. "Hybrid characteristics of heterogeneous traffic flow mixed with electric vehicles considering the amplitude of acceleration and deceleration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
    • Handle: RePEc:eee:phsmap:v:614:y:2023:i:c:s0378437123001115
    DOI: 10.1016/j.physa.2023.128556
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    5. Ren, Wenhao & Zhao, Xiaohua & Li, Haijian & Fu, Qiang, 2024. "Traffic flow impact of mixed heterogeneous platoons on highways: an approach combining driving simulation and microscopic traffic simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    6. Zhao, Jiaqi & Liang, Qinghuai & Guo, Jiaao & Pu, Keqian, 2024. "Vulnerability assessment and evolution analysis of Beijing's Urban Rail Transit Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 653(C).
    7. Li, Xia & Pang, Xiaomin & Zhang, Song & You, Zhijian & Ma, Xinwei & Chuo, Eryong, 2024. "Car-following model based on artificial potential field with consideration of horizontal curvature in connected vehicles environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 653(C).

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