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A bidirectional car-following model considering distance balance between adjacent vehicles

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Listed:
  • Yi, Ziwei
  • Lu, Wenqi
  • Qu, Xu
  • Gan, Jing
  • Li, Linheng
  • Ran, Bin

Abstract

Connected technology enhances the sensing capabilities of vehicles and enables them to access information not only from the front direction but also from the back direction. This paper presents a car-following model using the bidirectional information in the connected environment, called the bidirectional distance balanced model (BDBM). Based on the intelligent driver model (IDM), the proposed model balances the distance between the host vehicle and its nearest preceding and following vehicles. Then, the condition of local stability and string stability of the traffic flow is investigated by using the characteristic equation-based method (CEM). Furthermore, the efficiency formula of traffic flow obeys the BDBM is derived. Finally, simulations are conducted under the different settings of car-following scenarios with small perturbations. The theoretical and simulation results reveal that the string stability can be improved with the proportion of the information utilization item (IUI) increased. In addition, it is demonstrated that the proposed model is capable of maintaining traffic efficiency while enhancing traffic stability, which is superior to the IDM and the intelligent back-looking distance driver model (IBDM) Yi et al. (2020). All these findings provide a novel way to establish a car-following model improving traffic stability without affecting traffic efficiency.

Suggested Citation

  • Yi, Ziwei & Lu, Wenqi & Qu, Xu & Gan, Jing & Li, Linheng & Ran, Bin, 2022. "A bidirectional car-following model considering distance balance between adjacent vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    • Handle: RePEc:eee:phsmap:v:603:y:2022:i:c:s0378437122004149
    DOI: 10.1016/j.physa.2022.127606
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    References listed on IDEAS

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    1. Guo, Wenfeng & Song, Xiaolin & Cao, Haotian & Zhao, Song & Yi, Binlin & Wang, Jianqiang, 2023. "Human-centered driving authority allocation for driver-automation shared control: A two-layer game-theoretic approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).

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