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Modeling cars and trucks in the heterogeneous traffic based on car–truck combination effect using cellular automata

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  • Kong, Dewen
  • Sun, Lishan
  • Li, Jia
  • Xu, Yan

Abstract

An improved cellular automata model based on car–truck combination effect oriented to faithfully reproducing the characteristics of heterogeneous traffic flow is presented in this paper. The model focuses on reflecting complex car-following and lane-changing behavior of cars and trucks by incorporating the difference of physical performance and driving behavior between cars and trucks and the behavioral change caused by car–truck interaction. The update rules of cars and trucks were constructed separately based on the analysis results of NGSIM and SP survey. The impact of the truck in front was introduced into the model, which can affect the lane-changing probability, safety expectation and randomization probability. As revealed by the simulation results, the driving of vehicles in the heterogeneous traffic (i.e. car–truck, truck–car, car–car and truck–truck) is reproduced properly through the model. In addition, the model is capable of reproducing the hysteresis phenomenon and identifying three typical traffic states, namely free flow, synchronized flow and jam. Under certain conditions, trucks can help to eliminate the propagation of congestion waves. Moreover, the percentage of trucks and truck impact have a significant influence on the fundamental diagram, congestion rate, lane-changing rate and traffic stability. The findings of this study can provide a basis for further research on the heterogeneous traffic.

Suggested Citation

  • Kong, Dewen & Sun, Lishan & Li, Jia & Xu, Yan, 2021. "Modeling cars and trucks in the heterogeneous traffic based on car–truck combination effect using cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    • Handle: RePEc:eee:phsmap:v:562:y:2021:i:c:s0378437120307007
    DOI: 10.1016/j.physa.2020.125329
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    References listed on IDEAS

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    Cited by:

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    2. Shang, Xue-Cheng & Li, Xin-Gang & Xie, Dong-Fan & Jia, Bin & Jiang, Rui & Liu, Feng, 2022. "A data-driven two-lane traffic flow model based on cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    3. Jianjun Wang & Sai Wang & Xueqin Long & Dongyi Li & Chicheng Ma & Peng Li, 2022. "Ellipse-Like Radiation Range Grading Method of Traffic Accident Influence on Mountain Highways," Sustainability, MDPI, vol. 14(21), pages 1-21, October.
    4. Qiao, Yanfeng & Xue, Yu & Cen, Bingling & Zhang, Kun & Chen, Dong & Pan, Wei, 2024. "Study on particulate emission in two-lane mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    5. Zhang, Xiangzhou & Shi, Zhongke & Yu, Shaowei & Ma, Lijing, 2023. "A new car-following model considering driver’s desired visual angle on sharp curves," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    6. Kang, Chengjun & Qian, Yongsheng & Zeng, Junwei & Wei, Xuting & Zhang, Futao, 2024. "Analysis of stability, energy consumption and CO2 emissions in novel discrete-time car-following model with time delay under V2V environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    7. 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).
    8. 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).

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