A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems
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 f...
Gespeichert in:
Veröffentlicht in: | Sustainability 2024-08, Vol.16 (15), p.6696 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 15 |
container_start_page | 6696 |
container_title | Sustainability |
container_volume | 16 |
creator | Youn, Seok Jin Lee, Yong-Jae Han, Ha-Eun Lee, Chang-Woo Sohn, Donggyun Lee, Chulung |
description | 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. |
doi_str_mv | 10.3390/su16156696 |
format | Article |
fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_3090955840</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A804515586</galeid><sourcerecordid>A804515586</sourcerecordid><originalsourceid>FETCH-LOGICAL-c257t-119b75f85b496d86863df6e3000f7b8f73de720a422b2caed27bf2f09897b9573</originalsourceid><addsrcrecordid>eNpVkcFO3DAQhiNUpCLKpU9giVMrhdrx2o6PEbQUaQEJ2HM0ScbBKGuntlN1-wh96pouUsFzsDX6_t_6Z4riI6NnnGv6JS5MMiGllgfFUUUVKxkV9N2r9_viJMYnmg_nTDN5VPxpyAUkII2DaZdsHwm4gVxD_2gdkjVCcNaNpJnn4HOTJE8u8CdOfiZAHrB_dH7y447ceRi2MBPjA7nBX6m8RIcBkvWOrP1o4z_vTbKT_f1suHEDhjH4Jf92v4sJt_FDcWhginjych8Xm29fH86_l-vby6vzZl32lVCpZEx3SphadCsth1rWkg9GIs-hjOpqo_iAqqKwqqqu6gGHSnWmMlTXWnVaKH5cnO59c6QfC8bUPvkl5Pyx5VRTLUS9opk621MjTNhaZ3wK0OcacGt779DY3G9quhIsK2QWfHojyEzKgxhhibG9ur97y37es33wMQY07RzsFsKuZbR93mX7f5f8LxG6kNY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3090955840</pqid></control><display><type>article</type><title>A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>MDPI - Multidisciplinary Digital Publishing Institute</source><creator>Youn, Seok Jin ; Lee, Yong-Jae ; Han, Ha-Eun ; Lee, Chang-Woo ; Sohn, Donggyun ; Lee, Chulung</creator><creatorcontrib>Youn, Seok Jin ; Lee, Yong-Jae ; Han, Ha-Eun ; Lee, Chang-Woo ; Sohn, Donggyun ; Lee, Chulung</creatorcontrib><description>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.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su16156696</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Blockchain ; Chinese medicine ; Data analysis ; Data mining ; Decision making ; Equipment and supplies ; Forecasts and trends ; Green hydrogen ; Innovations ; Logistics ; Machine learning ; Materials handling ; Methods ; Photovoltaic cells ; R&D ; Research & development ; Research centers ; Technological planning ; Technology ; Trends</subject><ispartof>Sustainability, 2024-08, Vol.16 (15), p.6696</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c257t-119b75f85b496d86863df6e3000f7b8f73de720a422b2caed27bf2f09897b9573</cites><orcidid>0000-0002-7664-8001 ; 0000-0002-2041-0221</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids></links><search><creatorcontrib>Youn, Seok Jin</creatorcontrib><creatorcontrib>Lee, Yong-Jae</creatorcontrib><creatorcontrib>Han, Ha-Eun</creatorcontrib><creatorcontrib>Lee, Chang-Woo</creatorcontrib><creatorcontrib>Sohn, Donggyun</creatorcontrib><creatorcontrib>Lee, Chulung</creatorcontrib><title>A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems</title><title>Sustainability</title><description>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.</description><subject>Blockchain</subject><subject>Chinese medicine</subject><subject>Data analysis</subject><subject>Data mining</subject><subject>Decision making</subject><subject>Equipment and supplies</subject><subject>Forecasts and trends</subject><subject>Green hydrogen</subject><subject>Innovations</subject><subject>Logistics</subject><subject>Machine learning</subject><subject>Materials handling</subject><subject>Methods</subject><subject>Photovoltaic cells</subject><subject>R&D</subject><subject>Research & development</subject><subject>Research centers</subject><subject>Technological planning</subject><subject>Technology</subject><subject>Trends</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpVkcFO3DAQhiNUpCLKpU9giVMrhdrx2o6PEbQUaQEJ2HM0ScbBKGuntlN1-wh96pouUsFzsDX6_t_6Z4riI6NnnGv6JS5MMiGllgfFUUUVKxkV9N2r9_viJMYnmg_nTDN5VPxpyAUkII2DaZdsHwm4gVxD_2gdkjVCcNaNpJnn4HOTJE8u8CdOfiZAHrB_dH7y447ceRi2MBPjA7nBX6m8RIcBkvWOrP1o4z_vTbKT_f1suHEDhjH4Jf92v4sJt_FDcWhginjych8Xm29fH86_l-vby6vzZl32lVCpZEx3SphadCsth1rWkg9GIs-hjOpqo_iAqqKwqqqu6gGHSnWmMlTXWnVaKH5cnO59c6QfC8bUPvkl5Pyx5VRTLUS9opk621MjTNhaZ3wK0OcacGt779DY3G9quhIsK2QWfHojyEzKgxhhibG9ur97y37es33wMQY07RzsFsKuZbR93mX7f5f8LxG6kNY</recordid><startdate>20240801</startdate><enddate>20240801</enddate><creator>Youn, Seok Jin</creator><creator>Lee, Yong-Jae</creator><creator>Han, Ha-Eun</creator><creator>Lee, Chang-Woo</creator><creator>Sohn, Donggyun</creator><creator>Lee, Chulung</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>4U-</scope><scope>7X5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>K6~</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-7664-8001</orcidid><orcidid>https://orcid.org/0000-0002-2041-0221</orcidid></search><sort><creationdate>20240801</creationdate><title>A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems</title><author>Youn, Seok Jin ; Lee, Yong-Jae ; Han, Ha-Eun ; Lee, Chang-Woo ; Sohn, Donggyun ; Lee, Chulung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c257t-119b75f85b496d86863df6e3000f7b8f73de720a422b2caed27bf2f09897b9573</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Blockchain</topic><topic>Chinese medicine</topic><topic>Data analysis</topic><topic>Data mining</topic><topic>Decision making</topic><topic>Equipment and supplies</topic><topic>Forecasts and trends</topic><topic>Green hydrogen</topic><topic>Innovations</topic><topic>Logistics</topic><topic>Machine learning</topic><topic>Materials handling</topic><topic>Methods</topic><topic>Photovoltaic cells</topic><topic>R&D</topic><topic>Research & development</topic><topic>Research centers</topic><topic>Technological planning</topic><topic>Technology</topic><topic>Trends</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Youn, Seok Jin</creatorcontrib><creatorcontrib>Lee, Yong-Jae</creatorcontrib><creatorcontrib>Han, Ha-Eun</creatorcontrib><creatorcontrib>Lee, Chang-Woo</creatorcontrib><creatorcontrib>Sohn, Donggyun</creatorcontrib><creatorcontrib>Lee, Chulung</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>University Readers</collection><collection>Entrepreneurship Database</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Business Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Youn, Seok Jin</au><au>Lee, Yong-Jae</au><au>Han, Ha-Eun</au><au>Lee, Chang-Woo</au><au>Sohn, Donggyun</au><au>Lee, Chulung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems</atitle><jtitle>Sustainability</jtitle><date>2024-08-01</date><risdate>2024</risdate><volume>16</volume><issue>15</issue><spage>6696</spage><pages>6696-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><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.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su16156696</doi><orcidid>https://orcid.org/0000-0002-7664-8001</orcidid><orcidid>https://orcid.org/0000-0002-2041-0221</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2071-1050 |
ispartof | Sustainability, 2024-08, Vol.16 (15), p.6696 |
issn | 2071-1050 2071-1050 |
language | eng |
recordid | cdi_proquest_journals_3090955840 |
source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; MDPI - Multidisciplinary Digital Publishing Institute |
subjects | Blockchain Chinese medicine Data analysis Data mining Decision making Equipment and supplies Forecasts and trends Green hydrogen Innovations Logistics Machine learning Materials handling Methods Photovoltaic cells R&D Research & development Research centers Technological planning Technology Trends |
title | A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T12%3A25%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Data%20Analytics%20and%20Machine%20Learning%20Approach%20to%20Develop%20a%20Technology%20Roadmap%20for%20Next-Generation%20Logistics%20Utilizing%20Underground%20Systems&rft.jtitle=Sustainability&rft.au=Youn,%20Seok%20Jin&rft.date=2024-08-01&rft.volume=16&rft.issue=15&rft.spage=6696&rft.pages=6696-&rft.issn=2071-1050&rft.eissn=2071-1050&rft_id=info:doi/10.3390/su16156696&rft_dat=%3Cgale_proqu%3EA804515586%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3090955840&rft_id=info:pmid/&rft_galeid=A804515586&rfr_iscdi=true |