IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i6p2008-d767713.html
   My bibliography  Save this article

Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario

Author

Listed:
  • Xuan Fang

    (Department of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary)

  • Hexuan Li

    (Institute of Automotive Engineering, TU Graz, 8010 Graz, Austria)

  • Tamás Tettamanti

    (Department of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary)

  • Arno Eichberger

    (Institute of Automotive Engineering, TU Graz, 8010 Graz, Austria)

  • Martin Fellendorf

    (Institute of Highway Engineering and Transport Planning, TU Graz, 8010 Graz, Austria)

Abstract

There is consensus in industry and academia that Highly Automated Vehicles (HAV) and Connected Automated Vehicles (CAV) will be launched into the market in the near future due to emerging autonomous driving technology. In this paper, a mixed traffic simulation framework that integrates vehicle models with different automated driving systems in the microscopic traffic simulation was proposed. Currently, some of the more mature Automated Driving Systems (ADS) functions (e.g., Adaptive Cruise Control (ACC), Lane Keeping Assistant (LKA), etc.) are already equipped in vehicles, the very next step towards a higher automated driving is represented by Level 3 vehicles and CAV which show great promise in helping to avoid crashes, ease traffic congestion, and improve the environment. Therefore, to better predict and simulate the driving behavior of automated vehicles on the motorway scenario, a virtual test framework is proposed which includes the Highway Chauffeur (HWC) and Vehicle-to-Vehicle (V2V) communication function. These functions are implemented as an external driver model in PTV Vissim. The framework uses a detailed digital twin based on the M86 road network located in southwestern Hungary, which was constructed for autonomous driving tests. With this framework, the effect of the proposed vehicle models is evaluated with the microscopic traffic simulator PTV Vissim. A case study of the different penetration rates of HAV and CAV was performed on the M86 motorway. Preliminary results presented in this paper demonstrated that introducing HAV and CAV to the current network individually will cause negative effects on traffic performance. However, a certain ratio of mixed traffic, 60% CAV and 40% Human Driver Vehicles (HDV), could reduce this negative impact. The simulation results also show that high penetration CAV has fine driving stability and less travel delay.

Suggested Citation

  • Xuan Fang & Hexuan Li & Tamás Tettamanti & Arno Eichberger & Martin Fellendorf, 2022. "Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario," Energies, MDPI, vol. 15(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2008-:d:767713
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/6/2008/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/6/2008/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Martin Fellendorf & Peter Vortisch, 2010. "Microscopic Traffic Flow Simulator VISSIM," International Series in Operations Research & Management Science, in: Jaume Barceló (ed.), Fundamentals of Traffic Simulation, chapter 0, pages 63-93, Springer.
    2. Piotr Wróblewski & Wojciech Drożdż & Wojciech Lewicki & Paweł Miązek, 2021. "Methodology for Assessing the Impact of Aperiodic Phenomena on the Energy Balance of Propulsion Engines in Vehicle Electromobility Systems for Given Areas," Energies, MDPI, vol. 14(8), pages 1-24, April.
    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. Arno Eichberger & Zsolt Szalay & Martin Fellendorf & Henry Liu, 2022. "Advances in Automated Driving Systems," Energies, MDPI, vol. 15(10), pages 1-5, May.
    2. Mateusz Malarczyk & Jules-Raymond Tapamo & Marcin Kaminski, 2022. "Application of Neural Data Processing in Autonomous Model Platform—A Complex Review of Solutions, Design and Implementation," Energies, MDPI, vol. 15(13), pages 1-22, June.
    3. Andrea Gemma & Tina Onorato & Stefano Carrese, 2023. "Performances and Environmental Impacts of Connected and Autonomous Vehicles for Different Mixed-Traffic Scenarios," Sustainability, MDPI, vol. 15(13), pages 1-19, June.

    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. Mariusz Niekurzak & Jerzy Mikulik, 2021. "Modeling of Energy Consumption and Reduction of Pollutant Emissions in a Walking Beam Furnace Using the Expert Method—Case Study," Energies, MDPI, vol. 14(23), pages 1-22, December.
    2. Mariusz Niekurzak, 2021. "Determining the Unit Values of the Allocation of Greenhouse Gas Emissions for the Production of Biofuels in the Life Cycle," Energies, MDPI, vol. 14(24), pages 1-18, December.
    3. David Borge-Diez & Pedro Miguel Ortega-Cabezas & Antonio Colmenar-Santos & Jorge Juan Blanes-Peiró, 2021. "Contribution of Driving Efficiency to Vehicle-to-Building," Energies, MDPI, vol. 14(12), pages 1-30, June.
    4. Mariusz Niekurzak, 2021. "The Potential of Using Renewable Energy Sources in Poland Taking into Account the Economic and Ecological Conditions," Energies, MDPI, vol. 14(22), pages 1-17, November.
    5. Marcin Rabe & Agnieszka Jakubowska & Veselin Draskovic & Katarzyna Widera & Tomasz Pudło & Agnieszka Łopatka & Łukasz Kuźmiński, 2022. "Comparative Analysis on the Performance and Exhaust Gas Emission of Cars with Spark-Ignition Engines," Energies, MDPI, vol. 15(17), pages 1-18, August.
    6. Xavier Boulet & Mahdi Zargayouna & Gérard Scemama & Fabien Leurent, 2021. "A Middleware-Based Approach for Multi-Scale Mobility Simulation," Future Internet, MDPI, vol. 13(2), pages 1-21, January.
    7. Maria Cieśla & Elżbieta Macioszek, 2022. "The Perspective Projects Promoting Sustainable Mobility by Active Travel to School on the Example of the Southern Poland Region," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    8. Weiyi Lin & Han Zhao & Bingzhan Zhang & Ye Wang & Yan Xiao & Kang Xu & Rui Zhao, 2022. "Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast," Energies, MDPI, vol. 15(20), pages 1-27, October.
    9. Piotr Gołębiowski & Jolanta Żak & Ilona Jacyna-Gołda, 2020. "Approach to the Proecological Distribution of the Traffic Flow on the Transport Network from the Point of View of Carbon Dioxide," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    10. Muhammad Rizalul Wahid & Bentang Arief Budiman & Endra Joelianto & Muhammad Aziz, 2021. "A Review on Drive Train Technologies for Passenger Electric Vehicles," Energies, MDPI, vol. 14(20), pages 1-24, October.
    11. Shorshani, M. Fallah & Hatzopoulou, Marianne, 2016. "Microscopic Modeling for Atmospheric Urban Pollutant Dispersion in Dense Urban Road Networks," 57th Transportation Research Forum (51st CTRF) Joint Conference, Toronto, Ontario, May 1-4, 2016 319256, Transportation Research Forum.
    12. Elisabeth Bloder & Georg Jäger, 2021. "Is the Green Wave Really Green? The Risks of Rebound Effects When Implementing “Green” Policies," Sustainability, MDPI, vol. 13(10), pages 1-11, May.
    13. Umair Hasan & Andrew Whyte & Hamad AlJassmi, 2022. "A Microsimulation Modelling Approach to Quantify Environmental Footprint of Autonomous Buses," Sustainability, MDPI, vol. 14(23), pages 1-31, November.
    14. Lunacek, Monte & Williams, Lindy & Severino, Joseph & Ficenec, Karen & Ugirumurera, Juliette & Eash, Matthew & Ge, Yanbo & Phillips, Caleb, 2021. "A data-driven operational model for traffic at the Dallas Fort Worth International Airport," Journal of Air Transport Management, Elsevier, vol. 94(C).
    15. Enrica Carbone & Vinayak V. Dixit & E. Elisabet Rutstrom, 2022. "Should I stay or should I go? Congestion pricing and equilibrium selection in a transportation network," Theory and Decision, Springer, vol. 93(3), pages 535-562, October.
    16. Eimantas Neniškis & Arvydas Galinis & Egidijus Norvaiša, 2021. "Improving Transport Modeling in MESSAGE Energy Planning Model: Vehicle Age Distributions," Energies, MDPI, vol. 14(21), pages 1-16, November.
    17. Sung-Pil Hong & Kyung Min Kim & Suk-Joon Ko, 2021. "Estimating heterogeneous agent preferences by inverse optimization in a randomized nonatomic game," Annals of Operations Research, Springer, vol. 307(1), pages 207-228, December.
    18. Elżbieta Macioszek & Maria Cieśla & Anna Granà, 2023. "Future Development of an Energy-Efficient Electric Scooter Sharing System Based on a Stakeholder Analysis Method," Energies, MDPI, vol. 16(1), pages 1-24, January.
    19. Hollbeck, Gabor B. & Pilarczyk, René & Wang, Shanshan & Schreckenberg, Michael & Guhr, Thomas, 2024. "Congestions and spectral transitions in time-lagged correlations of motorway traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 649(C).
    20. Mostafa Shibl & Loay Ismail & Ahmed Massoud, 2021. "Electric Vehicles Charging Management Using Machine Learning Considering Fast Charging and Vehicle-to-Grid Operation," Energies, MDPI, vol. 14(19), pages 1-22, September.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jeners:v:15:y:2022:i:6:p:2008-:d:767713. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.