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An internal stochastic car-following model: Stochasticity analysis of mixed traffic environment

Author

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  • Mao, Peipei
  • Ji, Xinkai
  • Li, Shuo
  • Qu, Xu
  • Ran, Bin

Abstract

This paper investigates the impact of adaptive cruise control(ACC) vehicles on the stochasticity of human driving behavior by constructing a stochastic car-following model of human-driven vehicles (HDVs). Utilizing NGSIM dataset, the relationship between acceleration variance and space headway is analyzed, and a novel stochastic car-following model with headway is proposed to capture the internal stochasticity of drivers. Furthermore, the interaction between HDVs and AVs is explored by discussing stochasticity and stability in mixed traffic flow, using the proposed HDV model. The model parameters are calibrated based on NGSIM dataset and the simulation results indicate that the proposed stochastic car-following model can effectively reproduce the generation and propagation of traffic shocks without lane changes. Additionally, the simulations reveal that as the penetration rate of AVs increases in a lower range (0%–50%), the stochasticity of HDVs and stability in mixed traffic flow is substantially reduced. However, at higher penetration rates, increases in the AV penetration rate have a limited effect on the stochasticity of human driving behavior and the stability of mixed traffic flow. Concurrently, under conditions of low penetration rates, a smaller AV platoon size contributes more effectively to enhancing the stability of traffic flow and suppressing the stochastic behavior of HDVs. This research provides new insights for optimizing traffic flow control with automated vehicles.

Suggested Citation

  • Mao, Peipei & Ji, Xinkai & Li, Shuo & Qu, Xu & Ran, Bin, 2024. "An internal stochastic car-following model: Stochasticity analysis of mixed traffic environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 653(C).
    • Handle: RePEc:eee:phsmap:v:653:y:2024:i:c:s0378437124005600
    DOI: 10.1016/j.physa.2024.130051
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    References listed on IDEAS

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