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Modeling impacts of different data transmission delays on traffic jam, fuel consumption and emissions on curved road

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  • Ma, Guangyi
  • Li, Keping

Abstract

Under the intelligent transportation system, the use of autonomous vehicles can ease traffic jam, and reduce fuel consumption and emissions. The effects of different data transmission delays on traffic jam, fuel consumption and emissions on curved road are studied by constructing an extended multi-vehicle curve following model considering different data transmission delays to describe the curve following behavior of multiple autonomous vehicles. Subsequently, the stability of the novel model is derived by using linear stability theory. And then, numerical simulation is executed. Finally, some influence factors including curvature radius, backward looking effect, position signal time delay, and velocity signal time delay are discussed. Numerical simulation results show that reducing curvature radius and increasing backward looking effect can reduce fuel consumption and emissions, decrease density fluctuations, thereby gradually eliminating traffic jam. In addition, as difference between position signal time delay and velocity signal time delay decreases, the improvement degree in fuel consumption and emissions is limited. Meanwhile, it also makes density fluctuation decreases gradually, enhances traffic stream stability, and alleviates traffic jam, which is keeping with theoretical analysis. The finding has theoretical implications for designing autonomous vehicle control algorithms to reduce the negative effects of data transmission delays in traffic flow.

Suggested Citation

  • Ma, Guangyi & Li, Keping, 2024. "Modeling impacts of different data transmission delays on traffic jam, fuel consumption and emissions on curved road," Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:energy:v:310:y:2024:i:c:s0360544224029888
    DOI: 10.1016/j.energy.2024.133213
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    References listed on IDEAS

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    1. Yao, Zhihong & Wang, Yi & Liu, Bo & Zhao, Bin & Jiang, Yangsheng, 2021. "Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway," Energy, Elsevier, vol. 230(C).
    2. Ge, H.X. & Cheng, R.J. & Li, Z.P., 2008. "Two velocity difference model for a car following theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5239-5245.
    3. Liu, Lan & Zhu, Liling & Yang, Da, 2016. "Modeling and simulation of the car-truck heterogeneous traffic flow based on a nonlinear car-following model," Applied Mathematics and Computation, Elsevier, vol. 273(C), pages 706-717.
    4. Cheng-Ju Song & Hong-Fei Jia, 2022. "Car-Following Model Optimization and Simulation Based on Cooperative Adaptive Cruise Control," Sustainability, MDPI, vol. 14(21), pages 1-12, October.
    5. Wörz, Sascha & Bernhardt, Heinz, 2017. "A novel method for optimal fuel consumption estimation and planning for transportation systems," Energy, Elsevier, vol. 120(C), pages 565-572.
    6. Shuaiyang Jiao & Shengrui Zhang & Bei Zhou & Zixuan Zhang & Liyuan Xue, 2020. "An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    7. Qu, Xiaobo & Yu, Yang & Zhou, Mofan & Lin, Chin-Teng & Wang, Xiangyu, 2020. "Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach," Applied Energy, Elsevier, vol. 257(C).
    8. Yan-Tao Zhang & Yu-Zhang Chen & Cong-Ling Shi & Mao-Bin Hu, 2023. "Impact of vehicle platoon on energy and emission in mixed traffic environment," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(10), pages 1-14, October.
    Full references (including those not matched with items on IDEAS)

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