New feedback control for a novel two-dimensional lattice hydrodynamic model considering driver’s memory effect
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DOI: 10.1016/j.physa.2020.125295
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Cited by:
- 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).
- Zhai, Cong & Wu, Weitiao & Xiao, Yingping & Luo, Qiang & Zhang, Yusong, 2022. "Modeling bidirectional pedestrian flow with the perceived uncertainty of preceding pedestrian information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
- 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).
- 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.
- Yuan Tang & Yu Xue & Muyang Huang & Qiyun Wen & Bingling Cen & Dong Chen, 2023. "A Lattice Hydrodynamic Model for Four-Way Pedestrian Traffic with Turning Capacity," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
- 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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Keywords
Traffic flow; Two-dimensional; Lattice hydrodynamic model; Driver’s memory effect; Control signal;All these keywords.
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