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An extended lattice hydrodynamic model based on control theory considering the memory effect of flux difference

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  • Qin, Shunda
  • He, Zhiting
  • Cheng, Rongjun

Abstract

Nowadays, the memory effect of drivers’ behavior has been a hot topic in traffic flow research. In this paper, based on the lattice hydrodynamic model a new feedback control model is derived in a single-lane system. The memory effect of flux difference is considered in the new model to suppress the traffic jam. The critical condition of the model is analyzed by control method. The simulations are applied to verify the influence of feedback control signal on alleviating traffic jam. Besides, energy consumption simulation is designed in this paper. All the results demonstrate that the memory effect of flux difference model enhances the stability of traffic flow.

Suggested Citation

  • Qin, Shunda & He, Zhiting & Cheng, Rongjun, 2018. "An extended lattice hydrodynamic model based on control theory considering the memory effect of flux difference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 809-816.
    • Handle: RePEc:eee:phsmap:v:509:y:2018:i:c:p:809-816
    DOI: 10.1016/j.physa.2018.06.042
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    References listed on IDEAS

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    1. Ge, H.X. & Cheng, R.J. & Li, Z.P., 2008. "Two velocity difference model for a car following theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5239-5245.
    2. Kerner, Boris S., 2016. "Failure of classical traffic flow theories: Stochastic highway capacity and automatic driving," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 700-747.
    3. Nagatani, Takashi, 1998. "Modified KdV equation for jamming transition in the continuum models of traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 261(3), pages 599-607.
    4. Xin, Qi & Yang, Nan & Fu, Rui & Yu, Shaowei & Shi, Zhongke, 2018. "Impacts analysis of car following models considering variable vehicular gap policies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 338-355.
    5. Ou, Hui & Tang, Tie-Qiao, 2018. "An extended two-lane car-following model accounting for inter-vehicle communication," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 260-268.
    6. Cao, Bao-gui, 2015. "A new car-following model considering driver’s sensory memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 218-225.
    7. Zhao, Xiaomei & Gao, Ziyou, 2006. "A control method for congested traffic induced by bottlenecks in the coupled map car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 513-522.
    8. Tang, Tie-Qiao & Luo, Xiao-Feng & Zhang, Jian & Chen, Liang, 2018. "Modeling electric bicycle’s lane-changing and retrograde behaviors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1377-1386.
    9. Robert Herman & Elliott W. Montroll & Renfrey B. Potts & Richard W. Rothery, 1959. "Traffic Dynamics: Analysis of Stability in Car Following," Operations Research, INFORMS, vol. 7(1), pages 86-106, February.
    10. Zhu, H.B. & Dai, S.Q., 2008. "Numerical simulation of soliton and kink density waves in traffic flow with periodic boundaries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4367-4375.
    11. Zhang, H. M., 2003. "Driver memory, traffic viscosity and a viscous vehicular traffic flow model," Transportation Research Part B: Methodological, Elsevier, vol. 37(1), pages 27-41, January.
    12. Tang, Tie-Qiao & He, Jia & Yang, Shi-Chun & Shang, Hua-Yan, 2014. "A car-following model accounting for the driver’s attribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 583-591.
    13. Yu, Shaowei & Huang, Mengxing & Ren, Jia & Shi, Zhongke, 2016. "An improved car-following model considering velocity fluctuation of the immediately ahead car," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 1-17.
    14. Xue, Yu & Kang, San-Jun & Lu, Wei-Zhen & He, Hong-Di, 2014. "Energy dissipation of traffic flow at an on-ramp," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 172-178.
    15. Zhu, Wen-Xing & Zhang, Li-Dong, 2016. "Analysis of car-following model with cascade compensation strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 265-274.
    16. Tang, Tie-Qiao & Rui, Ying-Xu & Zhang, Jian & Shang, Hua-Yan, 2018. "A cellular automation model accounting for bicycle’s group behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1782-1797.
    17. Peng, Guanghan & Liu, Changqing & Tuo, Manxian, 2015. "Influence of the traffic interruption probability on traffic stability in lattice model for two-lane freeway," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 952-959.
    18. Yu, Lei & Li, Tong & Shi, Zhong-Ke, 2010. "Density waves in a traffic flow model with reaction-time delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(13), pages 2607-2616.
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    Cited by:

    1. Zhai, Cong & Zhang, Ronghui & Peng, Tao & Zhong, Changfu & Xu, Hongguo, 2023. "Heterogeneous lattice hydrodynamic model and jamming transition mixed with connected vehicles and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    2. Chen, Can & Ge, Hongxia & Cheng, Rongjun, 2019. "Self-stabilizing analysis of an extended car-following model with consideration of expected effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    3. Zhou, Jibiao & Chen, Siyuan & Ma, Changxi & Dong, Sheng, 2022. "Stability analysis of pedestrian traffic flow in horizontal channels: A numerical simulation method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    4. Sun, Yuqing & Ge, Hongxia & Cheng, Rongjun, 2019. "An extended car-following model considering driver’s memory and average speed of preceding vehicles with control strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 752-761.
    5. Sun, Yuqing & Ge, Hongxia & Cheng, Rongjun, 2019. "A car-following model considering the effect of electronic throttle opening angle over the curved road," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

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