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Feature Analysis on Mixed Traffic Flow of Manually Driven and Autonomous Vehicles Based on Cellular Automata

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  • Xinghua Hu
  • Mengyu Huang
  • Jianpu Guo

Abstract

This paper attempts to disclose the features of the mixed traffic flow of manually driven vehicles (MVs) and autonomous vehicles (AVs). Considering dynamic headway, the mixed traffic flow was modelled based on the improved single-land cellular automata (CA) traffic flow model (DHD) proposed by Zhang Ningxi. The established CA model was adopted to obtain the maximum flow of the mixed traffic flow and was analyzed under different proportions of AVs. On this basis, the features of the mixed traffic flow were summarized. The main results are as follows: the proportion of AVs has a significant impact on the mixed traffic flow; when the proportion reached 0.6, the flow of the whole lane was twice that of the MV traffic flow. At a low density, the AV proportion has an obvious influence on mixed traffic flow. At a high density, the mixed traffic flow changed very little, as the AV proportion increased from 0 to 5. The reason is that the flow of the whole lane is constrained by the fact that MVs cannot move faster. However, when the AV proportion reached 0.8, the flow of the whole lane became three times that at the proportion of 0.6. At the speed of 126 km/h, the flow rate was 2.5 times the speed limit of 54 km/h. The findings lay a theoretical basis for the modelling of multilane mixed traffic flow.

Suggested Citation

  • Xinghua Hu & Mengyu Huang & Jianpu Guo, 2020. "Feature Analysis on Mixed Traffic Flow of Manually Driven and Autonomous Vehicles Based on Cellular Automata," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-7, November.
    • Handle: RePEc:hin:jnlmpe:7210547
    DOI: 10.1155/2020/7210547
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    Cited by:

    1. Lyu, Zelin & Hu, Xiaojian & Zhang, Fang & Liu, Tenghui & Cui, Zhiwei, 2022. "Heterogeneous traffic flow characteristics on the highway with a climbing lane under different truck percentages: The framework of Kerner’s three-phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    2. Yao, Zhihong & Jin, Yuting & Jiang, Haoran & Hu, Lu & Jiang, Yangsheng, 2022. "CTM-based traffic signal optimization of mixed traffic flow with connected automated vehicles and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    3. Jiang, Yangsheng & Wang, Sichen & Yao, Zhihong & Zhao, Bin & Wang, Yi, 2021. "A cellular automata model for mixed traffic flow considering the driving behavior of connected automated vehicle platoons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    4. Vranken, Tim & Schreckenberg, Michael, 2022. "Modelling multi-lane heterogeneous traffic flow with human-driven, automated, and communicating automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    5. Bowen Gong & Fanting Wang & Ciyun Lin & Dayong Wu, 2022. "Modeling HDV and CAV Mixed Traffic Flow on a Foggy Two-Lane Highway with Cellular Automata and Game Theory Model," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    6. Li, Xia & Xiao, Yuewen & Zhao, Xiaodong & Ma, Xinwei & Wang, Xintong, 2023. "Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).

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