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A new two-lane lattice hydrodynamic model with the introduction of driver’s predictive effect

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  • Zhang, Jing
  • Xu, Keyu
  • Li, Shubin
  • Wang, Tao

Abstract

This paper devises a new two-lane lattice hydrodynamic model (TLHM) to explore driver’s predictive effect (DPE) on traffic oscillation. First, a linear approach is conducted to analytically predict the DPE on traffic performance. Theoretical analysis shows that with the help of DPE, the traffic flow stability will be gradually enhanced. Then, nonlinear analysis is implemented to explore the characteristics of traffic oscillation when sensitivity coefficient is near the critical point. The modified KdV equation derived from the new model and its analytical solution related kink–antikink density waves are obtained. Finally, numerical experiments show that the DPE can effectively dampen the growth of oscillation, which is well consistent with the theoretical analysis of the new model.

Suggested Citation

  • Zhang, Jing & Xu, Keyu & Li, Shubin & Wang, Tao, 2020. "A new two-lane lattice hydrodynamic model with the introduction of driver’s predictive effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    • Handle: RePEc:eee:phsmap:v:551:y:2020:i:c:s0378437120300650
    DOI: 10.1016/j.physa.2020.124249
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    References listed on IDEAS

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    Cited by:

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    3. Huimin Liu & Rongjun Cheng & Tingliu Xu, 2021. "Analysis of a Novel Two-Dimensional Lattice Hydrodynamic Model Considering Predictive Effect," Mathematics, MDPI, vol. 9(19), pages 1-13, October.
    4. Zhang, Yicai & Zhao, Min & Sun, Dihua & Liu, Xiaoyu & Huang, Shuai & Chen, Dong, 2022. "Robust H-infinity control for connected vehicles in lattice hydrodynamic model at highway tunnel," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    5. Nikita Madaan & Sapna Sharma, 2022. "Influence of driver’s behavior with empirical lane changing on the traffic dynamics," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(1), pages 1-11, January.
    6. Chen, Jin & Sun, Dihua & Zhao, Min & Li, Yang & Liu, Zhongcheng, 2021. "DCFS-based deep learning supervisory control for modeling lane keeping of expert drivers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).

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