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Carbon Emissions and Expressway Traffic Flow Patterns in China

Author

Listed:
  • Yaping Dong

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Jinliang Xu

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Xingliang Liu

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Chao Gao

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Han Ru

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Zhihao Duan

    (School of Highway, Chang’an University, Xi’an 710064, China)

Abstract

Traffic flow patterns severely impact vehicle carbon emissions. A field test was conducted in this study to obtain fuel consumption and traffic volume data under various traffic flow patterns and to explore the relationship between traffic flow patterns and vehicle carbon emissions. Carbon emission data were obtained via the indirect carbon emission accounting method proposed by the Intergovernmental Panel on Climate Change. Carbon emission prediction models for diesel trucks and gasoline passenger cars were established respectively with volume to capacity ratio as an explanatory variable. The results show that carbon emissions are highest under the congested flow conditions, followed by unstable flow, free flow, and steady flow. The relationship between the volume to capacity ratio and carbon emissions is a cubic curve function. The carbon emissions of trucks and passenger cars with a volume to capacity ratio of 0.4 to 0.5 are relatively small. The proposed carbon emissions models effectively quantify the carbon emissions of vehicles under different traffic flow patterns. The results of this study may provide data to support and a workable reference for expressway operation management and future low-carbon expressway expansion construction projects.

Suggested Citation

  • Yaping Dong & Jinliang Xu & Xingliang Liu & Chao Gao & Han Ru & Zhihao Duan, 2019. "Carbon Emissions and Expressway Traffic Flow Patterns in China," Sustainability, MDPI, vol. 11(10), pages 1-12, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2824-:d:232116
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    References listed on IDEAS

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    Cited by:

    1. Weiqiang Zhou & Haoxu Guo & Lihao Yao, 2023. "Statistical Modeling of Traffic Flow in Commercial Clusters Based on a Street Network," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    2. Hongxing Zhao & Ruichun He & Xiaoyan Jia, 2019. "Estimation and Analysis of Vehicle Exhaust Emissions at Signalized Intersections Using a Car-Following Model," Sustainability, MDPI, vol. 11(14), pages 1-25, July.
    3. Yaping Dong & Jinliang Xu & Chenwei Gu, 2020. "Modelling carbon emissions of diesel trucks on longitudinal slope sections in China," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-17, June.
    4. Jie Yang & Jinliang Xu & Chao Gao & Guohua Bai & Linfang Xie & Menghui Li, 2019. "Modeling of the Relationship Between Speed Limit and Characteristic Speed of Expressway Traffic Flow," Sustainability, MDPI, vol. 11(17), pages 1-12, August.
    5. Bu, Chujie & Cui, Xueqin & Li, Ruiyao & Li, Jin & Zhang, Yaxin & Wang, Can & Cai, Wenjia, 2021. "Achieving net-zero emissions in China’s passenger transport sector through regionally tailored mitigation strategies," Applied Energy, Elsevier, vol. 284(C).
    6. Natthakrit Bamrungwong & Varin Vongmanee & Wanchai Rattanawong, 2020. "The Development of a CO 2 Emission Coefficient for Medium- and Heavy-Duty Vehicles with Different Road Slope Conditions Using Multiple Linear Regression, and Considering the Health Effects," Sustainability, MDPI, vol. 12(17), pages 1-21, August.
    7. Jinliang Xu & Yaping Dong & Menghua Yan, 2020. "A Model for Estimating Passenger-Car Carbon Emissions that Accounts for Uphill, Downhill and Flat Roads," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
    8. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.

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