IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i15p6696-d1450235.html

A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems

Author

Listed:
  • Seok Jin Youn

    (Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea)

  • Yong-Jae Lee

    (Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea)

  • Ha-Eun Han

    (Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea)

  • Chang-Woo Lee

    (Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea)

  • Donggyun Sohn

    (Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea)

  • Chulung Lee

    (School of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea)

Abstract

The increasing density of urban populations has spurred interest in utilizing underground space. Underground logistics systems (ULS) are gaining traction due to their effective utilization of this space to enhance urban spatial efficiency. However, research on technological advancements in related fields remains limited. To address this gap, we applied a data-driven approach using patent data related to the ULS to develop a technology roadmap for the field. We employed Latent Dirichlet Allocation (LDA), a machine learning-based topic modeling technique, to categorize and identify six specific technology areas within the ULS domain. Subsequently, we conducted portfolio analytics to pinpoint technology areas with high technological value and to identify the major patent applicants in these areas. Finally, we assessed the technology market potential by mapping the technology life cycle for the identified high-value areas. Among the six technology areas identified, Topic 1 (Underground Material Handling System) and Topic 4 (Underground Transportation System) showed significant patent activity from companies and research institutions in China, the United States, South Korea, and Germany compared to other countries. These areas have the top 10 patent applicants, accounting for 20.8% and 13.6% of all patent applications, respectively. Additionally, technology life cycle analytics revealed a growth trajectory for these identified areas, indicating their rapid expansion and high innovation potential. This study provides a data-driven methodology to develop a technology roadmap that offers valuable insights for researchers, engineers, and policymakers in the ULS industry and supports informed decision-making regarding the field’s future direction.

Suggested Citation

  • Seok Jin Youn & Yong-Jae Lee & Ha-Eun Han & Chang-Woo Lee & Donggyun Sohn & Chulung Lee, 2024. "A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems," Sustainability, MDPI, vol. 16(15), pages 1-32, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6696-:d:1450235
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/15/6696/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/15/6696/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyewon Yang & Young Jae Han & Jiwon Yu & Sumi Kim & Sugil Lee & Gildong Kim & Chulung Lee, 2022. "Exploring Future Promising Technologies in Hydrogen Fuel Cell Transportation," Sustainability, MDPI, vol. 14(2), pages 1-19, January.
    2. Jaewoong Choi & Jiho Lee & Janghyeok Yoon & Sion Jang & Jaeyoung Kim & Sungchul Choi, 2022. "A two-stage deep learning-based system for patent citation recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6615-6636, November.
    3. Grimaldi, Michele & Cricelli, Livio & Di Giovanni, Martina & Rogo, Francesco, 2015. "The patent portfolio value analysis: A new framework to leverage patent information for strategic technology planning," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 286-302.
    4. Campbell, Richard S., 1983. "Patent trends as a technological forecasting tool," World Patent Information, Elsevier, vol. 5(3), pages 137-143.
    5. Wuyue An & Lin Wang & Dongfeng Zhang, 2023. "Comprehensive commodity price forecasting framework using text mining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1888, November.
    6. Koopo Kwon & Sungchan Jun & Yong-Jae Lee & Sanghei Choi & Chulung Lee, 2022. "Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap," Sustainability, MDPI, vol. 14(9), pages 1-30, April.
    7. Liangchao Huang & Zhengmeng Hou & Yanli Fang & Jianhua Liu & Tianle Shi, 2023. "Evolution of CCUS Technologies Using LDA Topic Model and Derwent Patent Data," Energies, MDPI, vol. 16(6), pages 1-14, March.
    8. Joule A. Bergerson & Adam Brandt & Joe Cresko & Michael Carbajales‐Dale & Heather L. MacLean & H. Scott Matthews & Sean McCoy & Marcelle McManus & Shelie A. Miller & William R. Morrow & I. Daniel Pose, 2020. "Life cycle assessment of emerging technologies: Evaluation techniques at different stages of market and technical maturity," Journal of Industrial Ecology, Yale University, vol. 24(1), pages 11-25, February.
    9. Bhatt, Priyanka C. & Lai, Kuei-Kuei & Drave, Vinayak A. & Lu, Tzu-Chuen & Kumar, Vimal, 2023. "Patent analysis based technology innovation assessment with the lens of disruptive innovation theory: A case of blockchain technological trajectories," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    10. Stefano Breschi & Rodolfo Helg, 1996. "Technological change and international competitiveness: the case of Switzerland," LIUC Papers in Economics 31, Cattaneo University (LIUC).
    11. Sanghoon Lee & Wonjoon Kim, 2017. "The knowledge network dynamics in a mobile ecosystem: a patent citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 717-742, May.
    12. Ronald Rousseau, 2019. "Balassa = revealed competitive advantage = activity," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1835-1836, December.
    13. Noh, Heeyong & Song, Young-Keun & Lee, Sungjoo, 2016. "Identifying emerging core technologies for the future: Case study of patents published by leading telecommunication organizations," Telecommunications Policy, Elsevier, vol. 40(10), pages 956-970.
    14. Dietmar Harhoff & Stefan Wagner, 2009. "The Duration of Patent Examination at the European Patent Office," Management Science, INFORMS, vol. 55(12), pages 1969-1984, December.
    15. Hairui Wei & Anlin Li & Nana Jia, 2020. "Research on Optimization and Design of Sustainable Urban Underground Logistics Network Framework," Sustainability, MDPI, vol. 12(21), pages 1-22, November.
    16. Jianjun Dong & Yuanxian Xu & Bon-gang Hwang & Rui Ren & Zhilong Chen, 2019. "The Impact of Underground Logistics System on Urban Sustainable Development: A System Dynamics Approach," Sustainability, MDPI, vol. 11(5), pages 1-21, February.
    17. Ghaffari, Mohsen & Aliahmadi, Alireza & Khalkhali, Abolfazl & Zakery, Amir & Daim, Tugrul U. & Yalcin, Haydar, 2023. "Topic-based technology mapping using patent data analysis: A case study of vehicle tires," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    18. Su, Yu-Shan & Huang, Hsini & Daim, Tugrul & Chien, Pan-Wei & Peng, Ru-Ling & Karaman Akgul, Arzu, 2023. "Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    19. Takalo, Tuomas & Kanniainen, Vesa, 2000. "Do patents slow down technological progress?: Real options in research, patenting, and market introduction," International Journal of Industrial Organization, Elsevier, vol. 18(7), pages 1105-1127, October.
    20. Song, Kisik & Kim, Karp Soo & Lee, Sungjoo, 2017. "Discovering new technology opportunities based on patents: Text-mining and F-term analysis," Technovation, Elsevier, vol. 60, pages 1-14.
    21. Zamani, Mehdi & Yalcin, Haydar & Naeini, Ali Bonyadi & Zeba, Gordana & Daim, Tugrul U, 2022. "Developing metrics for emerging technologies: identification and assessment," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    22. Markard, Jochen, 2020. "The life cycle of technological innovation systems," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    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. Yong-Jae Lee & Sung-Eun Park & Seong-Yeob Lee, 2024. "Machine Learning-Driven Topic Modeling and Network Analysis to Uncover Shared Knowledge Networks for Sustainable Korea–Japan Intangible Cultural Heritage Cooperation," Sustainability, MDPI, vol. 16(24), pages 1-38, December.
    2. Jiaojiao Li & Jianjun Dong & Rui Ren & Zhilong Chen, 2024. "Modeling Resilience of Metro-Based Urban Underground Logistics System Based on Multi-Layer Interdependent Network," Sustainability, MDPI, vol. 16(22), pages 1-23, November.
    3. Lee, Yong-Jae, 2025. "MIKTA maritime research gaps: Data-driven machine learning approach for sustainable collaboration," Journal of Transport Geography, Elsevier, vol. 128(C).
    4. Yong-Jae Lee, 2025. "Steering towards future sustainability: a data-driven roadmap for eco-friendly road transport research in the European context," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 30(6), pages 1-37, August.

    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. Lee, Yong-Jae, 2025. "MIKTA maritime research gaps: Data-driven machine learning approach for sustainable collaboration," Journal of Transport Geography, Elsevier, vol. 128(C).
    2. Yong-Jae Lee & Sung-Eun Park & Seong-Yeob Lee, 2024. "Machine Learning-Driven Topic Modeling and Network Analysis to Uncover Shared Knowledge Networks for Sustainable Korea–Japan Intangible Cultural Heritage Cooperation," Sustainability, MDPI, vol. 16(24), pages 1-38, December.
    3. Yong-Jae Lee, 2025. "Steering towards future sustainability: a data-driven roadmap for eco-friendly road transport research in the European context," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 30(6), pages 1-37, August.
    4. Ghaffari, Mohsen & Aliahmadi, Alireza & Khalkhali, Abolfazl & Zakery, Amir & Daim, Tugrul U. & Zamani, Mehdi, 2024. "Exploring the technological leaders using tire industry patents: A topic modeling approach," Technology in Society, Elsevier, vol. 78(C).
    5. Percia David, Dimitri & Maréchal, Loïc & Lacube, William & Gillard, Sébastien & Tsesmelis, Michael & Maillart, Thomas & Mermoud, Alain, 2023. "Measuring security development in information technologies: A scientometric framework using arXiv e-prints," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    6. Zhichao Ba & Kai Meng & Leilei Liu & Yujie Zhang, 2025. "How science-technology interactions affect technological innovation: the moderating role of topic divergence," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-20, December.
    7. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    8. Yong-Jae Lee & Young Jae Han & Sang-Soo Kim & Chulung Lee, 2022. "Patent Data Analytics for Technology Forecasting of the Railway Main Transformer," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    9. Han Zhang & Yongbo Lv & Jianwei Guo, 2022. "New Development Direction of Underground Logistics from the Perspective of Public Transport: A Systematic Review Based on Scientometrics," Sustainability, MDPI, vol. 14(6), pages 1-31, March.
    10. Chen, Fan & Wang, Delu & Li, Chunxiao & Mao, Jinqi & Wang, Yadong & Yu, Lan, 2025. "Evolution analysis and trend forecasting for low-carbon technologies in coal power based on a three-layer text mining framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 217(C).
    11. Huang, Kenneth G. & Su, Yu-Shan & Chen, Jin & Kajikawa, Yuya, 2025. "Shaping the future through developing and managing breakthrough innovations: A new conceptual framework," Technological Forecasting and Social Change, Elsevier, vol. 214(C).
    12. Yonghe Lu & Xinyu Tong & Xin Xiong & Hou Zhu, 2024. "Knowledge graph enhanced citation recommendation model for patent examiners," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(4), pages 2181-2203, April.
    13. Podrecca, Matteo & Culot, Giovanna & Tavassoli, Sam & Orzes, Guido, 2024. "Artificial intelligence for climate change: a patent analysis in the manufacturing sector," Papers in Innovation Studies 2024/12, Lund University, CIRCLE - Centre for Innovation Research.
    14. Pantano, Eleonora & Priporas, Constantinos-Vasilios & Stylos, Nikolaos, 2018. "Knowledge Push Curve (KPC) in retailing: Evidence from patented innovations analysis affecting retailers' competitiveness," Journal of Retailing and Consumer Services, Elsevier, vol. 44(C), pages 150-160.
    15. Li, Rongrong & Chen, Qian & Wang, Qiang, 2025. "Emerging topic identification in carbon capture utilization and storage (CCUS) for advancing climate action (SDG 13)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 222(C).
    16. Ruilian Han & Lu An & Wei Zhou & Gang Li, 2025. "Field identification and opportunity discovery of photovoltaics technology: deep transfer learning method," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(8), pages 4283-4307, August.
    17. Fu, Zhenkang & Zhu, Qinghua & Liu, Bingxiang & Yan, Chungen, 2025. "Patent lifespan prediction and interpreting the key determinants: An application of interpretable machine learning survival analysis approach," Technological Forecasting and Social Change, Elsevier, vol. 215(C).
    18. Yun, Siyeong & Song, Kisik & Kim, Chulhyun & Lee, Sungjoo, 2021. "From stones to jewellery: Investigating technology opportunities from expired patents," Technovation, Elsevier, vol. 103(C).
    19. Jungpyo Lee & So Young Sohn, 2017. "What makes the first forward citation of a patent occur earlier?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 279-298, October.
    20. Alptekin Durmuşoğlu, 2017. "Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences," Sustainability, MDPI, vol. 9(5), pages 1-10, May.

    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:16:y:2024:i:15:p:6696-:d:1450235. 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.