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Fuel consumption at the oversaturated signalized intersection considering queue effects: A case study in Harbin, China

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  • Wu, Lina
  • Ci, Yusheng
  • Wang, Yunpeng
  • Chen, Peng

Abstract

Vehicles are always in an alternating state of going and stopping at oversaturated signalized intersections, which not only reduces the operational efficiency of the intersections but also greatly increases the average fuel consumption of vehicles. The purpose of this study is to propose a fuel consumption analysis method that can be adapted to the oversaturated signalized intersections. Considering queue effects, this paper proposed a way to define the judging criteria and the classification for the oversaturated state based on the intersection spacing, traffic demand, and the intersection capacity. The theoretical models were constructed for estimating vehicular fuel consumption towards two different oversaturated states and with/without automatic start and stop systems respectively, which was dependent on two main parameters of the delay and the stop that were confirmed by the definite number theory. The results show that the degree of saturation of the approach and the automatic start and stop systems have a great influence on vehicular fuel consumption at oversaturated signalized intersections, and the shorter spacing is expected for the oversaturated state II because of more deceleration and acceleration maneuvers.

Suggested Citation

  • Wu, Lina & Ci, Yusheng & Wang, Yunpeng & Chen, Peng, 2020. "Fuel consumption at the oversaturated signalized intersection considering queue effects: A case study in Harbin, China," Energy, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:energy:v:192:y:2020:i:c:s0360544219323497
    DOI: 10.1016/j.energy.2019.116654
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    References listed on IDEAS

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    1. Qian Zhang & Xunmin Ou & Xiaoyu Yan & Xiliang Zhang, 2017. "Electric Vehicle Market Penetration and Impacts on Energy Consumption and CO 2 Emission in the Future: Beijing Case," Energies, MDPI, vol. 10(2), pages 1-15, February.
    2. Wang, Hailin & Ou, Xunmin & Zhang, Xiliang, 2017. "Mode, technology, energy consumption, and resulting CO2 emissions in China's transport sector up to 2050," Energy Policy, Elsevier, vol. 109(C), pages 719-733.
    3. Tang, Tie-Qiao & Yi, Zhi-Yan & Lin, Qing-Feng, 2017. "Effects of signal light on the fuel consumption and emissions under car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 200-205.
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    Citations

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    Cited by:

    1. Liu, Ying & Lin, Boqiang & Xu, Bin, 2021. "Modeling the impact of energy abundance on economic growth and CO2 emissions by quantile regression: Evidence from China," Energy, Elsevier, vol. 227(C).
    2. Yongyi Li & Wei Yang & Xiaorui Zhang & Xi Kang & Mengfei Li, 2022. "Research on Automatic Driving Trajectory Planning and Tracking Control Based on Improvement of the Artificial Potential Field Method," Sustainability, MDPI, vol. 14(19), pages 1-28, September.
    3. 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.
    4. Wang, Xiaoning & Liu, Minzhuang & Ci, Yusheng & Wu, Lina, 2022. "Effect of front two adjacent vehicles’ velocity information on car-following model construction and stability analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    5. Zeng, Jimin & Liu, Lidong & Liang, Xiao & Chen, Shihe & Yuan, Jun, 2021. "Evaluating fuel consumption factor for energy conservation and carbon neutral on an industrial thermal power unit," Energy, Elsevier, vol. 232(C).
    6. Sun, Bin & Zhang, Qijun & Wei, Ning & Jia, Zhenyu & Li, Chunming & Mao, Hongjun, 2022. "The energy flow of moving vehicles for different traffic states in the intersection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).

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