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The effects of velocity difference changes with memory on the dynamics characteristics and fuel economy of traffic flow

Author

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  • Yu, Shaowei
  • Zhao, Xiangmo
  • Xu, Zhigang
  • Zhang, Licheng

Abstract

To evaluate the effects of velocity difference changes with memory in the intelligent transportation environment on the dynamics and fuel consumptions of traffic flow, we first investigate the linkage between velocity difference changes with memory and car-following behaviors with the measured data in cities, and then propose an improved cooperative car-following model considering multiple velocity difference changes with memory in the cooperative adaptive cruise control strategy, finally carry out several numerical simulations under the periodic boundary condition and at signalized intersections to explore how velocity difference changes with memory affect car’s velocity, velocity fluctuation, acceleration and fuel consumptions in the intelligent transportation environment. The results show that velocity difference changes with memory have obvious effects on car-following behaviors, that the improved cooperative car-following model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion, that the stability and fuel economy of traffic flow simulated by the improved car-following model with velocity difference changes with memory is obviously superior to those without velocity difference changes, and that taking velocity difference changes with memory into account in designing the advanced adaptive cruise control strategy can significantly improve the stability and fuel economy of traffic flow.

Suggested Citation

  • Yu, Shaowei & Zhao, Xiangmo & Xu, Zhigang & Zhang, Licheng, 2016. "The effects of velocity difference changes with memory on the dynamics characteristics and fuel economy of traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 613-628.
    • Handle: RePEc:eee:phsmap:v:461:y:2016:i:c:p:613-628
    DOI: 10.1016/j.physa.2016.06.060
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    Citations

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    Cited by:

    1. Xin, Qi & Fu, Rui & Yuan, Wei & Liu, Qingling & Yu, Shaowei, 2018. "Predictive intelligent driver model for eco-driving using upcoming traffic signal information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 806-823.
    2. Liao, Peng & Tang, Tie-Qiao & Wang, Tao & Zhang, Jian, 2019. "A car-following model accounting for the driving habits," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 108-118.
    3. Junyan Han & Xiaoyuan Wang & Huili Shi & Bin Wang & Gang Wang & Longfei Chen & Quanzheng Wang, 2022. "Research on the Impacts of Vehicle Type on Car-Following Behavior, Fuel Consumption and Exhaust Emission in the V2X Environment," Sustainability, MDPI, vol. 14(22), pages 1-15, November.
    4. Ma, Xinjuan & Ge, Hongxia & Cheng, Rongjun, 2019. "Influences of acceleration with memory on stability of traffic flow and vehicle’s fuel consumption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 143-154.
    5. 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.
    6. Ou, Hui & Tang, Tie-Qiao & Rui, Ying-Xu & Zhou, Jie-Ming, 2018. "Modeling electric bicycle’s abnormal behavior at a signalized intersection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 218-231.
    7. Yu, Bin & Zhou, Huixin & Wang, Lin & Wang, Zirui & Cui, Shaohua, 2021. "An extended two-lane car-following model considering the influence of heterogeneous speed information on drivers with different characteristics under honk environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    8. Jia, Yanfeng & Qu, Dayi & Song, Hui & Wang, Tao & Zhao, Zixu, 2022. "Car-following characteristics and model of connected autonomous vehicles based on safe potential field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    9. Wang, Pengcheng & Yu, Guizhen & Wu, Xinkai & Qin, Hongmao & Wang, Yunpeng, 2018. "An extended car-following model to describe connected traffic dynamics under cyberattacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 351-370.
    10. Yang, Qiaoli & Shi, Zhongke, 2018. "Effects of the design of waiting areas on the dynamic behavior of queues at signalized intersections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 181-195.
    11. Kekun Zhang & Dayi Qu & Hui Song & Tao Wang & Shouchen Dai, 2022. "Analysis of Lane-Changing Decision-Making Behavior and Molecular Interaction Potential Modeling for Connected and Automated Vehicles," Sustainability, MDPI, vol. 14(17), pages 1-20, September.

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