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. Fatemeh Enayatollahi & Ahmed Osman Idris & M. A. Amiri Atashgah, 2019. "Modelling bus bunching under variable transit demand using cellular automata," Public Transport, Springer, vol. 11(2), pages 269-298, August.
    3. 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.
    4. Abdelghany, Ahmed & Abdelghany, Khaled & Mahmassani, Hani, 2016. "A hybrid simulation-assignment modeling framework for crowd dynamics in large-scale pedestrian facilities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 159-176.
    5. 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.
    6. 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.
    7. 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.
    8. Monika Ziemska-Osuch & Dawid Osuch, 2022. "Modeling the Assessment of Intersections with Traffic Lights and the Significance Level of the Number of Pedestrians in Microsimulation Models Based on the PTV Vissim Tool," Sustainability, MDPI, vol. 14(14), pages 1-11, July.
    9. Demin Nalic & Aleksa Pandurevic & Arno Eichberger & Branko Rogic, 2020. "Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems," Sustainability, MDPI, vol. 12(24), pages 1-12, December.
    10. 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.
    11. Taghreed Alghamdi & Sifatul Mostafi & Ghadeer Abdelkader & Khalid Elgazzar, 2022. "A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior," Future Internet, MDPI, vol. 14(10), pages 1-21, October.
    12. 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.
    13. 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.
    14. Walter Cardoso Satyro & Jose Celso Contador & Jose Luiz Contador & Marco Aurélio Fragomeni & Sonia Francisca de Paula Monken & Ana Freitas Ribeiro & Anderson Ferreira de Lima & Jansen Anderson Gomes &, 2021. "Implementing Industry 4.0 through Cleaner Production and Social Stakeholders: Holistic and Sustainable Model," Sustainability, MDPI, vol. 13(22), pages 1-16, November.
    15. Jingzheng Fan & Bingfeng Zu & Jianwei Zhou & Zhen Wang & Haopeng Wang, 2021. "Adaptive Mode Selection Strategy for Series-Parallel Hybrid Electric Vehicles Based on Variable Power Reserve," Energies, MDPI, vol. 14(11), pages 1-18, May.
    16. Manfred Dollinger & Gerhard Fischerauer, 2021. "Model-Based Range Prediction for Electric Cars and Trucks under Real-World Conditions," Energies, MDPI, vol. 14(18), pages 1-27, September.
    17. Adam Sulich & Letycja Sołoducho-Pelc, 2022. "Changes in Energy Sector Strategies: A Literature Review," Energies, MDPI, vol. 15(19), pages 1-26, September.
    18. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 14(22), pages 1-22, November.
    19. 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.
    20. 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.

    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.