IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v605y2022ics0378437122006434.html
   My bibliography  Save this article

The energy flow of moving vehicles for different traffic states in the intersection

Author

Listed:
  • Sun, Bin
  • Zhang, Qijun
  • Wei, Ning
  • Jia, Zhenyu
  • Li, Chunming
  • Mao, Hongjun

Abstract

To clarify the impact of steady traffic (ST) and stop-go traffic (SGT) on the energy flow of moving vehicles contributes to a deeper understanding of signalized intersection’s traffic optimization mechanism. In this paper, the theoretical model of vehicle energy was improved based on vehicle driving states, 3 energy evaluation indexes were proposed, and 6 vehicle cycles experiments were carried out in ST and SGT. The results show that: (1) The internal energy consumption of vehicles is the main direction of the total tank energy flow of vehicles, ST reduces the flow intensity of total tank energy to internal energy by 1.2% and increases the distribution force to friction energy consumption by 22.9%, which makes the energy distribution of moving vehicles more uniform. (2) ST weakens the influence of speed on air resistance energy consumption by 59.1%, and its regulatory effect on air resistance energy consumption is stronger than that of friction energy consumption. (3) Energy conservation equation of moving vehicles is formed and coefficients’ physical significance is explained. The study provides a theoretical basis for the optimization and management of signalized intersections.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:605:y:2022:i:c:s0378437122006434
    DOI: 10.1016/j.physa.2022.128025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122006434
    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.2022.128025?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. Arshad Jamal & Muhammad Tauhidur Rahman & Hassan M. Al-Ahmadi & Irfan Ullah & Muhammad Zahid, 2020. "Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    2. Xu, Xiaodan & Aziz, H.M. Abdul & Liu, Haobing & Rodgers, Michael O. & Guensler, Randall, 2020. "A scalable energy modeling framework for electric vehicles in regional transportation networks," Applied Energy, Elsevier, vol. 269(C).
    3. 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).
    4. Li, Yongfu & Zhong, Zhenyu & Zhang, Kaibi & Zheng, Taixiong, 2019. "A car-following model for electric vehicle traffic flow based on optimal energy consumption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    5. 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.
    6. Vittorio Astarita & Vincenzo Pasquale Giofrè & Giuseppe Guido & Alessandro Vitale, 2019. "A Single Intersection Cooperative-Competitive Paradigm in Real Time Traffic Signal Settings Based on Floating Car Data," Energies, MDPI, vol. 12(3), pages 1-22, January.
    7. Hyeonjik Lee & Kihyung Lee, 2020. "Comparative Evaluation of the Effect of Vehicle Parameters on Fuel Consumption under NEDC and WLTP," Energies, MDPI, vol. 13(16), pages 1-19, August.
    8. Sergey Chuprov & Ilya Viksnin & Iuliia Kim & Egor Marinenkov & Maria Usova & Eduard Lazarev & Timofey Melnikov & Danil Zakoldaev, 2019. "Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System," Energies, MDPI, vol. 12(23), pages 1-19, November.
    9. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    10. Mohammed Al-Turki & Arshad Jamal & Hassan M. Al-Ahmadi & Mohammed A. Al-Sughaiyer & Muhammad Zahid, 2020. "On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia," Sustainability, MDPI, vol. 12(18), pages 1-24, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).

    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. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    2. Arshad Jamal & Waleed Umer, 2020. "Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network," IJERPH, MDPI, vol. 17(20), pages 1-22, October.
    3. Jun Du & Bin Jia & Shiteng Zheng, 2022. "Stability Analysis of Continuous Stochastic Linear Model," Sustainability, MDPI, vol. 14(5), pages 1-13, March.
    4. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.
    5. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    6. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    7. Yavas, Volkan & Yavaş Tez, Özge, 2023. "Consumer intention over upcoming utopia: Urban air mobility," Journal of Air Transport Management, Elsevier, vol. 107(C).
    8. 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.
    9. 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).
    10. Dai, Ziyi & Liu, Haobing & Rodgers, Michael O. & Guensler, Randall, 2022. "Electric vehicle market potential and associated energy and emissions reduction benefits," Applied Energy, Elsevier, vol. 322(C).
    11. Barouch Giechaskiel & Dimitrios Komnos & Georgios Fontaras, 2021. "Impacts of Extreme Ambient Temperatures and Road Gradient on Energy Consumption and CO 2 Emissions of a Euro 6d-Temp Gasoline Vehicle," Energies, MDPI, vol. 14(19), pages 1-20, September.
    12. Mariano Gallo & Mario Marinelli, 2020. "Sustainable Mobility: A Review of Possible Actions and Policies," Sustainability, MDPI, vol. 12(18), pages 1-39, September.
    13. Moneim Massar & Imran Reza & Syed Masiur Rahman & Sheikh Muhammad Habib Abdullah & Arshad Jamal & Fahad Saleh Al-Ismail, 2021. "Impacts of Autonomous Vehicles on Greenhouse Gas Emissions—Positive or Negative?," IJERPH, MDPI, vol. 18(11), pages 1-23, May.
    14. Zeng, Junwei & Qian, Yongsheng & Li, Jiao & Zhang, Yongzhi & Xu, Dejie, 2023. "Congestion and energy consumption of heterogeneous traffic flow mixed with intelligent connected vehicles and platoons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    15. Zvonimir Dabčević & Branimir Škugor & Jakov Topić & Joško Deur, 2022. "Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology," Energies, MDPI, vol. 15(11), pages 1-21, June.
    16. Angel Jaramillo-Alcazar & Jaime Govea & William Villegas-Ch, 2023. "Advances in the Optimization of Vehicular Traffic in Smart Cities: Integration of Blockchain and Computer Vision for Sustainable Mobility," Sustainability, MDPI, vol. 15(22), pages 1-23, November.
    17. Karol Tucki & Olga Orynycz & Antoni Świć & Mateusz Mitoraj-Wojtanek, 2019. "The Development of Electromobility in Poland and EU States as a Tool for Management of CO 2 Emissions," Energies, MDPI, vol. 12(15), pages 1-22, July.
    18. 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.
    19. Wang, An & Xu, Junshi & Zhang, Mingqian & Zhai, Zhiqiang & Song, Guohua & Hatzopoulou, Marianne, 2022. "Emissions and fuel consumption of a hybrid electric vehicle in real-world metropolitan traffic conditions," Applied Energy, Elsevier, vol. 306(PB).
    20. Polychronis Spanoudakis & Gerasimos Moschopoulos & Theodoros Stefanoulis & Nikolaos Sarantinoudis & Eftichios Papadokokolakis & Ioannis Ioannou & Savvas Piperidis & Lefteris Doitsidis & Nikolaos C. Ts, 2020. "Efficient Gear Ratio Selection of a Single-Speed Drivetrain for Improved Electric Vehicle Energy Consumption," Sustainability, MDPI, vol. 12(21), pages 1-19, November.

    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:605:y:2022:i:c:s0378437122006434. 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.