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Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway

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  • Yao, Zhihong
  • Wang, Yi
  • Liu, Bo
  • Zhao, Bin
  • Jiang, Yangsheng

Abstract

The application of connected and automated vehicles can significantly reduce traffic congestion, fuel consumption, and transportation emissions. Most existing studies on connected and automated vehicles focus on improving traffic efficiency; the impact on fuel consumption and transportation emissions is not concerned. This study evaluates the influence of connected and automated vehicles on fuel consumption and emissions of mixed traffic flow on the expressway. Firstly, fuel consumption and transportation emissions models are introduced. Secondly, three car-following models are employed to capture the car-following behaviors in the mixed traffic flow. Then, a numerical simulation is designed to investigate the influence of connected and automated vehicles on fuel consumption and transportation emissions of mixed traffic flow. Finally, some factors that impacted fuel consumption and transportation emissions of mixed traffic flow are discussed. The simulation results show that connected automated vehicles can significantly reduce fuel consumption and transportation emissions. The maximum reduction percentages of HC, NOx, CO, and fuel consumption are 24.33%, 27.06%, 37.53%, and 40.58%, respectively, at 100% penetration rate of connected automated vehicles. Moreover, the parameters of the car-following models have a significant influence on fuel consumption and transportation emissions. The result indicates that the design of more minor expected headway would significantly improve the energy-saving and emissions reduction effect of connected automated vehicles.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:230:y:2021:i:c:s0360544221010148
    DOI: 10.1016/j.energy.2021.120766
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    3. Junyan Han & Xiaoyuan Wang & Gang Wang, 2022. "Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review," Sustainability, MDPI, vol. 14(13), pages 1-27, July.
    4. Yang, Yichen & Li, Zuxing & Li, Yabin & Cao, Tianyu & Li, Zhipeng, 2023. "Stability enhancement for traffic flow via self–stabilizing control strategy in the presence of packet loss," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    5. He, Yongming & Kang, Jia & Pei, Yulong & Ran, Bin & Song, Yuting, 2021. "Research on influencing factors of fuel consumption on superhighway based on DEMATEL-ISM model," Energy Policy, Elsevier, vol. 158(C).
    6. Peng, Guanghan & Luo, Chunli & Zhao, Hongzhuan & Tan, Huili, 2023. "Jamming transition in two-lane lattice model integrating the deception attacks on influx during the lane-changing process under vehicle to everything environment," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    7. Jiang, Yangsheng & Ren, Tingting & Ma, Yuqin & Wu, Yunxia & Yao, Zhihong, 2023. "Traffic safety evaluation of mixed traffic flow considering the maximum platoon size of connected automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    8. Kwangho Ko & Tongwon Lee & Seunghyun Jeong, 2021. "A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data," Sustainability, MDPI, vol. 13(20), pages 1-15, October.
    9. Ma, Jie & Xu, Mengmeng & Jiang, Jiehui, 2023. "Mapping high-resolution urban road carbon and pollutant emissions using travel demand data," Energy, Elsevier, vol. 263(PE).
    10. Jiang, Yangsheng & Cong, Hongwei & Wang, Yi & Wu, Yunxia & Li, Hongwu & Yao, Zhihong, 2023. "A new control strategy of CAVs platoon for mitigating traffic oscillation in a two-lane highway," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    11. Yao, Zhihong & Gu, Qiufan & Jiang, Yangsheng & Ran, Bin, 2022. "Fundamental diagram and stability of mixed traffic flow considering platoon size and intensity of connected automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    12. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    13. Chen, Shuiwang & Hu, Lu & Yao, Zhihong & Zhu, Juanxiu & Zhao, Bin & Jiang, Yangsheng, 2022. "Efficient and environmentally friendly operation of intermittent dedicated lanes for connected autonomous vehicles in mixed traffic environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P2).
    14. Jiang, Yangsheng & Wang, Sichen & Yao, Zhihong & Zhao, Bin & Wang, Yi, 2021. "A cellular automata model for mixed traffic flow considering the driving behavior of connected automated vehicle platoons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).

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