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Benefit of connectivity on promoting stability and capacity of traffic flow in automation era: An analytical and numerical investigation

Author

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  • Dong, Jiakuan
  • Luo, Dongyu
  • Gao, Zhijun
  • Wang, Jiangfeng
  • Chen, Lei

Abstract

With the emergence of connected and automated vehicles (CAVs), understanding the impact of such technological advances on traffic flow is vital to prepare for the popularity of CAVs. It has been well documented in the literature that the connectivity feature of CAVs will further benefit the traffic flow by enhancing stability and improving capacity in comparison to automated vehicles (AVs). However, there is little analytical and quantitative evidence to support this statement. In this paper, we attempt to bridge this gap and reveal the potential of connectivity for CAVs in promoting stability and capacity. First, we use a general feedback control structure to model the longitudinal dynamic of AVs. A feedforward link is added to the general structure of AVs to represent the connectivity feature of CAVs. Then, the performance of AVs and CAVs in attenuating disturbance is compared. Moreover, the time headway boundary to maintain string stability is defined and used for capacity analysis. The analytical and numerical results show that (1) the string stable region is enlarged and the potential to attenuate disturbance is promoted with proper tuning of the feedforward gain; (2) the employable time headway is reduced by introducing the communication link. This paper presents a unified framework and a practical procedure for demonstrating the benefit of connectivity on CAVs. The findings in this paper can be served as an analytical foundation and extended with other elaborate models to further understand the benefit of connectivity for CAVs.

Suggested Citation

  • Dong, Jiakuan & Luo, Dongyu & Gao, Zhijun & Wang, Jiangfeng & Chen, Lei, 2023. "Benefit of connectivity on promoting stability and capacity of traffic flow in automation era: An analytical and numerical investigation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    • Handle: RePEc:eee:phsmap:v:629:y:2023:i:c:s0378437123007252
    DOI: 10.1016/j.physa.2023.129170
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    References listed on IDEAS

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    1. Wang, Yan & Qin, Yanyan & Luo, Zhongbin & Wang, Hao, 2025. "Capacity improvement for mixed traffic flow by installing vehicle-to-vehicle devices on partial human-driven vehicles to serve connected automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
    2. Liu, Hang & Zou, Zhiyun & Jiang, Zehao & Chen, Yujiang & Yang, Qingmei & Gao, Jianzhi, 2024. "Method for utilizing the reserved lane capacity: Formation of the mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 648(C).
    3. Zong, Ting & Li, Yan & Qin, Yanyan, 2024. "Enhancing stability of traffic flow mixed with connected automated vehicles via enabling partial regular vehicles with vehicle-to-vehicle communication function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    4. Zhang, Jing & Gao, Qian & Tian, Junfang & Cui, Fengying & Wang, Tao, 2024. "Car-following model based on spatial expectation effect in connected vehicle environment: modeling, stability analysis and identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    5. Wang, Yi & Zhou, Chenjing & Zeng, Yanjie & Gao, Yacong & Rong, Jian & Xiao, Yang, 2025. "Modeling of car-following behavior at signalized intersection considering vehicle-vehicle and vehicle-road interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 678(C).

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