IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2022i1p672-d1020506.html

Millimetre Wave and Sub-6 5G Readiness of Mobile Network Big Data for Public Transport Planning

Author

Listed:
  • Okkie Putriani

    (Department of Civil Engineering, Universitas Atma Jaya Yogykarta, Yogyakarta 55281, Indonesia
    Department of Civil and Environmental Engineering, Universitas Gadjah Mada, Yogyakarta 55284, Indonesia)

  • Sigit Priyanto

    (Department of Civil and Environmental Engineering, Universitas Gadjah Mada, Yogyakarta 55284, Indonesia)

  • Imam Muthohar

    (Department of Civil and Environmental Engineering, Universitas Gadjah Mada, Yogyakarta 55284, Indonesia)

  • Mukhammad Rizka Fahmi Amrozi

    (Department of Civil and Environmental Engineering, Universitas Gadjah Mada, Yogyakarta 55284, Indonesia)

Abstract

The need to solve public transport planning challenges using 5G is demanding. In 2019, the world started using 5G technology. Unfortunately, many countries have no equipment that is compatible with 5G infrastructures. There are two main deployment options for countries willing to accept 5G. They can directly venture to install relatively expensive infrastructure, called 5G SA (standalone access). However, more countries use the 5G NSA (non-standalone access) alternative, a 5G network supported by existing 4G infrastructure. One of the considerations for choosing NSA 5G is that it still performs 4G equalisation in its area. The data throughput is faster but still uses the leading 4G network. Interestingly, there are three types of 5G: low-band (sub-6), middle-band (sub-6), and high-band (millimetre-wave (mmWave)). The problem is determining the kind of 5G needed for public transport planning. Meanwhile, mobile network big data (MNBD) requires robust and stable internet access, with broad coverage in real time. MNBD movement includes the movement of people and vehicles, as well as logistics. GPS and internet connections track the activity of private vehicles and public transportation. The difference between mmWave and sub-6 5G can complement transportation planning needs. The density and height of buildings in urban areas and the affordability of the range of the connections determine 5G. This study examines the literature on 5G and then, using the bibliographic method, matches the network coverage obtained in Indonesia using nPerf data services. According to the data, urban areas are becoming more densely populated. Thus, this could show the differences in the data quality outside of metropolitan areas. This study also discusses the current conditions in terms of market potential and the development of smart cities and provides an overview of how real-time mobile data can support public transport planning. This article provides beneficial insight into the stability and adjustment of 5G, where the connectivity can be adequately maintained so that the MNBD can deliver representative data for analysis.

Suggested Citation

  • Okkie Putriani & Sigit Priyanto & Imam Muthohar & Mukhammad Rizka Fahmi Amrozi, 2022. "Millimetre Wave and Sub-6 5G Readiness of Mobile Network Big Data for Public Transport Planning," Sustainability, MDPI, vol. 15(1), pages 1-19, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:672-:d:1020506
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/672/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/1/672/
    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. Leonardo Guevara & Fernando Auat Cheein, 2020. "The Role of 5G Technologies: Challenges in Smart Cities and Intelligent Transportation Systems," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    3. John Zacharias & Xinyi Liu, 2022. "The Role of the Access Environment in Metro Commute Travel Satisfaction," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    4. Ali Gohar & Gianfranco Nencioni, 2021. "The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System," Sustainability, MDPI, vol. 13(9), pages 1-24, May.
    5. Carlos Oliveira Cruz & Joaquim Miranda Sarmento, 2020. "“Mobility as a Service” Platforms: A Critical Path towards Increasing the Sustainability of Transportation Systems," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    6. Neal R. Haddaway & Matthew J. Page & Chris C. Pritchard & Luke A. McGuinness, 2022. "PRISMA2020: An R package and Shiny app for producing PRISMA 2020‐compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis," Campbell Systematic Reviews, John Wiley & Sons, vol. 18(2), June.
    7. Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
    8. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    9. Peraphan Jittrapirom & Valeria Caiati & Anna-Maria Feneri & Shima Ebrahimigharehbaghi & María J. Alonso González & Jishnu Narayan, 2017. "Mobility as a Service: A Critical Review of Definitions, Assessments of Schemes, and Key Challenges," Urban Planning, Cogitatio Press, vol. 2(2), pages 13-25.
    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. Andrzej Paszkiewicz & Bartosz Pawłowicz & Bartosz Trybus & Mateusz Salach, 2021. "Traffic Intersection Lane Control Using Radio Frequency Identification and 5G Communication," Energies, MDPI, vol. 14(23), pages 1-17, December.
    2. Alper Ozpinar, 2023. "A Hyper-Integrated Mobility as a Service (MaaS) to Gamification and Carbon Market Enterprise Architecture Framework for Sustainable Environment," Energies, MDPI, vol. 16(5), pages 1-22, March.
    3. Amitkumar V. Jha & Bhargav Appasani & Mohammad S. Khan & Sherali Zeadally & Iyad Katib, 2024. "6G for intelligent transportation systems: standards, technologies, and challenges," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 86(2), pages 241-268, June.
    4. Feng, Hailin & Lv, Haibin & Lv, Zhihan, 2023. "Resilience towarded Digital Twins to improve the adaptability of transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    5. Constantin Aurelian Ionescu & Melinda Timea Fülöp & Dan Ioan Topor & Sorinel Căpușneanu & Teodora Odett Breaz & Sorina Geanina Stănescu & Mihaela Denisa Coman, 2021. "The New Era of Business Digitization through the Implementation of 5G Technology in Romania," Sustainability, MDPI, vol. 13(23), pages 1-23, December.
    6. Iria Lopez-Carreiro & Andres Monzon & Elena Lopez, 2023. "MaaS Implications in the Smart City: A Multi-Stakeholder Approach," Sustainability, MDPI, vol. 15(14), pages 1-27, July.
    7. Georgina Santos & Nikolay Nikolaev, 2021. "Mobility as a Service and Public Transport: A Rapid Literature Review and the Case of Moovit," Sustainability, MDPI, vol. 13(7), pages 1-18, March.
    8. Marit K. Natvig & Erlend Stav & Audun Vennesland, 2025. "Systematic mapping of scientific publications on MaaS," Management Review Quarterly, Springer, vol. 75(1), pages 83-118, February.
    9. 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).
    10. Stan Hurn & Jing Tian & Lina Xu, 2021. "Assessing the Informational Content of Official Australian Bureau of Meteorology Forecasts of Wind Speed," The Economic Record, The Economic Society of Australia, vol. 97(319), pages 525-547, December.
    11. Brave, Scott A. & Gascon, Charles & Kluender, William & Walstrum, Thomas, 2021. "Predicting benchmarked US state employment data in real time," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1261-1275.
    12. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    13. Kalyani Ganpat Midgule & Prasoom Dwivedi, 2026. "Research hotspots in agricultural finance research: a bibliometric exploration study," Future Business Journal, Springer, vol. 12(1), pages 1-19, December.
    14. Liu, Jianing & Wen, Xiao & Jian, Sisi, 2024. "Toward better equity: Analyzing travel patterns through a neural network approach in mobility-as-a-service," Transport Policy, Elsevier, vol. 153(C), pages 110-126.
    15. Aastveit, Knut Are & Trovik, Tørres, 2014. "Estimating the output gap in real time: A factor model approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 180-193.
    16. Ruhrort, Lisa, 2020. "Reassessing the Role of Shared Mobility Services in a Transport Transition: Can They Contribute the Rise of an Alternative Socio-Technical Regime of Mobility?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(19), pages 1-1.
    17. Cristina Regueiro & Santiago de Diego & Borja Urkizu, 2025. "Leveraging Blockchain Technology for Secure 5G Offloading Processes," Future Internet, MDPI, vol. 17(5), pages 1-31, April.
    18. Chenhao Zhu & Jonah Susskind & Mario Giampieri & Hazel Backus O’Neil & Alan M. Berger, 2023. "Optimizing Sustainable Suburban Expansion with Autonomous Mobility through a Parametric Design Framework," Land, MDPI, vol. 12(9), pages 1-31, September.
    19. Pär Stockhammar & Pär Österholm, 2018. "Do inflation expectations granger cause inflation?," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 35(2), pages 403-431, August.
    20. Benjamin Maas, 2022. "Literature Review of Mobility as a Service," Sustainability, MDPI, vol. 14(14), pages 1-28, July.

    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:jsusta:v:15:y:2022:i:1:p:672-:d:1020506. 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.