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Modeling the impact of road network configuration on vehicle energy consumption

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  • Luin, B.
  • Petelin, S.
  • Al Mansour, F.

Abstract

The paper shows that road geometry has a great impact on overall fuel consumption and emissions. Some roads connect traffic origins and destinations directly, while some take winding, indirect routes. Indirect connections result in longer distances driven and increased fuel consumption. A similar effect is observed on congested roads with stop and go traffic and on mountain roads with many changes in elevation. In this light, we propose a methodology for analysis of road networks based on energy consumed by the vehicles and the energy needed to build more efficient connections. This framework takes into consideration traffic volume, shares of vehicle classes, road geometry and energy needed for road operation and construction. Its application was illustrated through two case studies, one with macroscopic traffic data and one with microscopic traffic simulation that can also be applied for urban road network optimization.

Suggested Citation

  • Luin, B. & Petelin, S. & Al Mansour, F., 2017. "Modeling the impact of road network configuration on vehicle energy consumption," Energy, Elsevier, vol. 137(C), pages 260-271.
    • Handle: RePEc:eee:energy:v:137:y:2017:i:c:p:260-271
    DOI: 10.1016/j.energy.2017.06.138
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Costagliola, Maria Antonietta & Costabile, Marianeve & Prati, Maria Vittoria, 2018. "Impact of road grade on real driving emissions from two Euro 5 diesel vehicles," Applied Energy, Elsevier, vol. 231(C), pages 586-593.
    2. Sun, Bin & Zhang, Qijun & Hu, Le & Zou, Chao & Wei, Ning & Jia, Zhenyu & Zhao, Xiaoyang & Peng, Jianfei & Mao, Hongjun & Wu, Zhong, 2023. "A prediction-evaluation method for road network energy consumption: Fusion of vehicle energy flow principle and Two-Fluid theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    3. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    4. Yavasoglu, H.A. & Tetik, Y.E. & Gokce, K., 2019. "Implementation of machine learning based real time range estimation method without destination knowledge for BEVs," Energy, Elsevier, vol. 172(C), pages 1179-1186.
    5. Hugo Ferreira & Carlos Manuel Rodrigues & Carlos Pinho, 2019. "Impact of Road Geometry on Vehicle Energy Consumption and CO 2 Emissions: An Energy-Efficiency Rating Methodology," Energies, MDPI, vol. 13(1), pages 1-27, December.
    6. Kroyan, Yuri & Wojcieszyk, Michal & Kaario, Ossi & Larmi, Martti & Zenger, Kai, 2020. "Modeling the end-use performance of alternative fuels in light-duty vehicles," Energy, Elsevier, vol. 205(C).
    7. Hariharan, C. & Gunadevan, D. & Arun Prakash, S. & Latha, K. & Antony Aroul Raj, V. & Velraj, R., 2022. "Simulation of battery energy consumption in an electric car with traction and HVAC model for a given source and destination for reducing the range anxiety of the driver," Energy, Elsevier, vol. 249(C).

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