IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i18p6579-d910168.html
   My bibliography  Save this article

Proposals for Using the Advanced Tools of Communication between Autonomous Vehicles and Infrastructure in Selected Cases

Author

Listed:
  • Michał Zawodny

    (Faculty of Civil Engineering, Wrocław University of Science and Technology (Politechnika Wrocławska), 50-370 Wrocław, Poland)

  • Maciej Kruszyna

    (Faculty of Civil Engineering, Wrocław University of Science and Technology (Politechnika Wrocławska), 50-370 Wrocław, Poland)

Abstract

The purpose of this paper is to describe solutions to yet unsolved problems of autonomous vehicles and infrastructure communication via the Internet of Things (IoT). The paper, in the form of a conceptual article, intentionally does not contain research elements, as we plan to conduct simulations in future papers. Each of the many forms of communication between vehicles and infrastructure (V2I) or vice versa offers different possibilities. Here, we describe typical situations and challenges related to the introduction of autonomous vehicles in traffic. An investment in V2I may be necessary to keep the traffic of autonomous vehicles safe, smooth, and energy efficient. Based on the review of existing solutions, we propose several ideas, key elements, algorithms, and hardware. Merely detecting the road infrastructure may not be enough. It is also necessary to consider a new form of travel called the Personal Transporter (PT). The introduction of new systems and solutions offers benefits for both autonomous vehicles and vehicles with a low degree of automation.

Suggested Citation

  • Michał Zawodny & Maciej Kruszyna, 2022. "Proposals for Using the Advanced Tools of Communication between Autonomous Vehicles and Infrastructure in Selected Cases," Energies, MDPI, vol. 15(18), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6579-:d:910168
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/18/6579/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/18/6579/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Timothy F. Welch & Alyas Widita, 2019. "Big data in public transportation: a review of sources and methods," Transport Reviews, Taylor & Francis Journals, vol. 39(6), pages 795-818, November.
    2. Cuauhtemoc Anda & Alexander Erath & Pieter Jacobus Fourie, 2017. "Transport modelling in the age of big data," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(0), pages 19-42, August.
    3. Crawford, F. & Watling, D.P. & Connors, R.D., 2018. "Identifying road user classes based on repeated trip behaviour using Bluetooth data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 55-74.
    4. Griffin, Greg Phillip & Mulhall, Megan & Simek, Chris & Riggs, William W., 2020. "Mitigating Bias in Big Data for Transportation," SocArXiv trbv9, Center for Open Science.
    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. Kevin Credit & Zander Arnao, 2023. "A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data," Environment and Planning B, , vol. 50(3), pages 709-722, March.
    2. Alattar, Mohammad Anwar & Cottrill, Caitlin & Beecroft, Mark, 2021. "Public participation geographic information system (PPGIS) as a method for active travel data acquisition," Journal of Transport Geography, Elsevier, vol. 96(C).
    3. Bantis, Thanos & Haworth, James, 2020. "Assessing transport related social exclusion using a capabilities approach to accessibility framework: A dynamic Bayesian network approach," Journal of Transport Geography, Elsevier, vol. 84(C).
    4. Gleb V. Savin, 2021. "The smart city transport and logistics system: Theory, methodology and practice," Upravlenets, Ural State University of Economics, vol. 12(6), pages 67-86, October.
    5. Kandt, Jens & Leak, Alistair, 2019. "Examining inclusive mobility through smartcard data: What shall we make of senior citizens' declining bus patronage in the West Midlands?," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    6. Erick Yohanes Kalengkongan & Wilson Bogar & Fitri H. Mamonto, 2022. "The Quality of Vehicles' Public Service Testing in The Tomohon Transportation Department," Technium Social Sciences Journal, Technium Science, vol. 32(1), pages 62-75, June.
    7. Yadi Zhu & Feng Chen & Ming Li & Zijia Wang, 2018. "Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data," Sustainability, MDPI, vol. 10(11), pages 1-17, November.
    8. Baochao Li & Xiaoshu Cao & Jianbin Xu & Wulin Wang & Shishu Ouyang & Dan Liu, 2021. "Spatial–Temporal Pattern and Influence Factors of Land Used for Transportation at the County Level since the Implementation of the Reform and Opening-Up Policy in China," Land, MDPI, vol. 10(8), pages 1-17, August.
    9. Shaw, F. Atiyya & Wang, Xinyi & Mokhtarian, Patricia L. & Watkins, Kari E., 2021. "Supplementing transportation data sources with targeted marketing data: Applications, integration, and internal validation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 149(C), pages 150-169.
    10. Jiping Cao & Hartwig H. Hochmair & Fisal Basheeh, 2022. "The Effect of Twitter App Policy Changes on the Sharing of Spatial Information through Twitter Users," Geographies, MDPI, vol. 2(3), pages 1-14, September.
    11. Yang, Hongtai & Ping, An & Wei, Hongmin & Zhai, Guocong, 2023. "Unique in the metro system: The likelihood to re-identify a metro user with limited trajectory points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    12. Chu, Chih-Peng & Chou, Yu-Hsin, 2021. "Using cellular data to analyze the tourists' trajectories for tourism destination attributes: A case study in Hualien, Taiwan," Journal of Transport Geography, Elsevier, vol. 96(C).
    13. Wei, Ming, 2022. "Investigating the influence of weather on public transit passenger’s travel behaviour: Empirical findings from Brisbane, Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 36-51.
    14. Liao, Cong & Scheuer, Bronte, 2022. "Evaluating the performance of transit-oriented development in Beijing metro station areas: Integrating morphology and demand into the node-place model," Journal of Transport Geography, Elsevier, vol. 100(C).
    15. Karner, Alex, 2021. "People-focused and Near-term Public Transit Performance Analysis," SocArXiv kd6bq, Center for Open Science.
    16. Tae-Hyoung Tommy Gim, 2018. "An Analysis of the Relationship between Land Use and Weekend Travel: Focusing on the Internal Capture of Trips," Sustainability, MDPI, vol. 10(2), pages 1-17, February.
    17. Cong Liao & Teqi Dai, 2022. "Is “Attending Nearby School” Near? An Analysis of Travel-to-School Distances of Primary Students in Beijing Using Smart Card Data," Sustainability, MDPI, vol. 14(7), pages 1-12, April.
    18. Pizzol, Bruna & Strambi, Orlando & Giannotti, Mariana & Arbex, Renato Oliveira & Alves, Bianca Bianchi, 2021. "Activity behavior of residents of Paraisópolis slum: Analysis of multiday activity patterns using data collected with smartphones," Journal of choice modelling, Elsevier, vol. 39(C).
    19. Chen, Ruoyu & Zhou, Jiangping, 2022. "Fare adjustment’s impacts on travel patterns and farebox revenue: An empirical study based on longitudinal smartcard data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 111-133.
    20. repec:thr:techub:10032:y:2022:i:1:p:62-75 is not listed on IDEAS
    21. Chung, Sai-Ho, 2021. "Applications of smart technologies in logistics and transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).

    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:18:p:6579-:d:910168. 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.