IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v626y2023ics0378437123006325.html
   My bibliography  Save this article

A prediction-evaluation method for road network energy consumption: Fusion of vehicle energy flow principle and Two-Fluid theory

Author

Listed:
  • Sun, Bin
  • Zhang, Qijun
  • Hu, Le
  • Zou, Chao
  • Wei, Ning
  • Jia, Zhenyu
  • Zhao, Xiaoyang
  • Peng, Jianfei
  • Mao, Hongjun
  • Wu, Zhong

Abstract

Urban road traffic is a significant source of energy consumption and emissions in the transportation sector. Implementing energy-saving and emission-reduction technology requires further development of systems for predicting and evaluating the energy consumption of the road network. This paper constructs an urban road network traffic energy consumption prediction model (S-RNEM), which couples the principle of vehicle energy flow with the Two-Fluid model based on the proposed traffic flow decomposition assumption. According to the observed data for light and heavy vehicles, road network energy consumption evaluation index e is developed via parameters n, p. The results of the three road network structures demonstrate that decreasing the design speed and the number of lanes of high-energy-consumption roads may reduce the total energy consumption of the road network by 12% and 4%, respectively, and that the former’s energy-saving impact is better than the latter. The evaluation index e can accurately measure the energy consumption of the road network. Energy consumption growth is positively associated with the value of e when e is bigger than 0. Energy consumption growth is inversely related to the value of e when e is smaller than 0. The S-RNEM has an excellent prediction accuracy, evidenced by the average absolute percentage error of 14.2% for sections and 1.5% for the road network between the predicted value and the actual energy consumption. This research can assist in developing energy-saving and emission-reduction technologies for urban road traffic.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:626:y:2023:i:c:s0378437123006325
    DOI: 10.1016/j.physa.2023.129077
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123006325
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2023.129077?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Zhaoze, Liu & Rongjun, Cheng & Hongxia, Ge, 2019. "Research on preceding vehicle’s taillight effect and energy consumption in an extended macro traffic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 304-314.
    3. Robert Herman & Siamak Ardekani, 1984. "Characterizing Traffic Conditions in Urban Areas," Transportation Science, INFORMS, vol. 18(2), pages 101-140, May.
    4. Williams, James C. & Mahmassani, Hani S. & Herman, Robert, 1995. "Sampling strategies for two-fluid model parameter estimation in urban networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 29(3), pages 229-244, May.
    5. Chai, Jian & Yang, Ying & Wang, Shouyang & Lai, Kin Keung, 2016. "Fuel efficiency and emission in China's road transport sector: Induced effect and rebound effect," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 188-197.
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hyungwoo Lim & Jaehyeok Kim & Ha-Hyun Jo, 2020. "Population Age Structure and Greenhouse Gas Emissions from Road Transportation: A Panel Cointegration Analysis of 21 OECD Countries," IJERPH, MDPI, vol. 17(21), pages 1-18, October.
    2. Yuan, Zhen & Xu, Jie & Li, Bing & Yao, Tingting, 2022. "Limits of technological progress in controlling energy consumption: Evidence from the energy rebound effects across China's industrial sector," Energy, Elsevier, vol. 245(C).
    3. Daganzo, Carlos F., 2011. "On the macroscopic stability of freeway traffic," Transportation Research Part B: Methodological, Elsevier, vol. 45(5), pages 782-788, June.
    4. Williams, James C. & Mahmassani, Hani S. & Herman, Robert, 1995. "Sampling strategies for two-fluid model parameter estimation in urban networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 29(3), pages 229-244, May.
    5. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    6. Amini, Behnam & Shahi, Jalil & Ardekani, Siamak A., 1998. "An observational study of the network-level traffic variables," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(4), pages 271-278, May.
    7. Liu, Ronghui & May, Tony & Shepherd, Simon, 2011. "On the fundamental diagram and supply curves for congested urban networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(9), pages 951-965, November.
    8. 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).
    9. Shoshanna Saxe & Dena Kasraian, 2020. "Rethinking environmental LCA life stages for transport infrastructure to facilitate holistic assessment," Journal of Industrial Ecology, Yale University, vol. 24(5), pages 1031-1046, October.
    10. Aghamohammadi, Rafegh & Laval, Jorge A., 2020. "Dynamic traffic assignment using the macroscopic fundamental diagram: A Review of vehicular and pedestrian flow models," Transportation Research Part B: Methodological, Elsevier, vol. 137(C), pages 99-118.
    11. 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.
    12. D'Adamo, Idiano & Gastaldi, Massimo & Rosa, Paolo, 2020. "Recycling of end-of-life vehicles: Assessing trends and performances in Europe," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    13. Daganzo, Carlos F., 2010. "On the Stability of Freeway Traffic," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4vf597r5, Institute of Transportation Studies, UC Berkeley.
    14. Li, Guohao & Niu, Miaomiao & Xiao, Jin & Wu, Jiaqian & Li, Jinkai, 2023. "The rebound effect of decarbonization in China’s power sector under the carbon trading scheme," Energy Policy, Elsevier, vol. 177(C).
    15. 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.
    16. Gayah, Vikash V. & Daganzo, Carlos F., 2011. "Clockwise hysteresis loops in the Macroscopic Fundamental Diagram: An effect of network instability," Transportation Research Part B: Methodological, Elsevier, vol. 45(4), pages 643-655, May.
    17. Geroliminis, Nikolas & Daganzo, Carlos F., 2008. "Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 759-770, November.
    18. Geroliminis, Nikolaos, 2007. "Increasing mobility in cities by controlling overcrowding," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt5wg9j6z7, Institute of Transportation Studies, UC Berkeley.
    19. Estrella Trincado & Antonio Sánchez-Bayón & José María Vindel, 2021. "The European Union Green Deal: Clean Energy Wellbeing Opportunities and the Risk of the Jevons Paradox," Energies, MDPI, vol. 14(14), pages 1-23, July.
    20. Stamos, Iraklis & Salanova Grau, Josep Maria & Mitsakis, Evangelos, 2013. "Μακροσκοπικά Θεμελιώδη Διαγράμματα: Ευρήματα Μέσω Προσομοίωσης Για Το Οδικό Δίκτυο Της Θεσσαλονίκης [Macroscopic fundamental diagrams: Simulation based findings from the road network of Thessalonik," MPRA Paper 61538, University Library of Munich, Germany.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:626:y:2023:i:c:s0378437123006325. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.