Identification of Promising Vacant Technologies for the Development of Truck on Freight Train Transportation Systems
In this study, we identify promising, currently vacant technologies for a Truck on Flatcar or Truck on Freight Train (TFTFT) system by analyzing the relevant patent information. We then apply network analysis from macro- and microperspectives to establish technology development strategies. We first...
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MDPI AG
2021-01-01
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author | Sungchan Jun Seong Ho Han Jiwon Yu Jumi Hwang Sangbaek Kim Chulung Lee |
author_facet | Sungchan Jun Seong Ho Han Jiwon Yu Jumi Hwang Sangbaek Kim Chulung Lee |
author_sort | Sungchan Jun |
collection | DOAJ |
description | In this study, we identify promising, currently vacant technologies for a Truck on Flatcar or Truck on Freight Train (TFTFT) system by analyzing the relevant patent information. We then apply network analysis from macro- and microperspectives to establish technology development strategies. We first researched the patent database from the United States Patent and Trademark Office (USPTO) by extracting relevant keywords for the TFTFT system. We then preprocessed the patent data to develop a patent-International Patent Classification (IPC) matrix and a patent-keyword matrix. Next, we developed a generative topographic mapping (GTM)-based patent map using the patent-IPC matrix and detected any patent vacuums. Then, in order to confirm the promising patent vacuums, we technically examined criticality and trend analyses. Finally, we designed an IPC-based network and a keyword network with promising patent vacuums to derive a technology development strategy from a macro- and microperspective for the TFTFT system. As a result, we confirmed two promising patent vacuums. The patent vacuums found were defined as the technical field of rail vehicles suitable for TFTFT systems and the technical field of equipment and systems for freight transfer to rail vehicles. The proposed procedure and analysis method provide useful insights for developing a research and development (R&D) strategy and technology development strategy for a TFTFT system. |
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issn | 2076-3417 |
language | English |
last_indexed | 2024-03-09T05:56:29Z |
publishDate | 2021-01-01 |
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spelling | doaj.art-a4e5aeab6e5344cda41a68609f4159bc2023-12-03T12:13:42ZengMDPI AGApplied Sciences2076-34172021-01-0111249910.3390/app11020499Identification of Promising Vacant Technologies for the Development of Truck on Freight Train Transportation SystemsSungchan Jun0Seong Ho Han1Jiwon Yu2Jumi Hwang3Sangbaek Kim4Chulung Lee5Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, KoreaLogistics System Research Team, Korea Railroad Research Institute, 176 Cheoldobangmulgwan-ro, Uiwang-si 16105, Gyeonggi, KoreaDepartment of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, KoreaDepartment of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, KoreaDepartment of Packaging, Yonsei University, 1, Yeonsedae-gil, Heungeop-myeon, Wonju-si 26493, Gangwon-do, KoreaSchool of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, KoreaIn this study, we identify promising, currently vacant technologies for a Truck on Flatcar or Truck on Freight Train (TFTFT) system by analyzing the relevant patent information. We then apply network analysis from macro- and microperspectives to establish technology development strategies. We first researched the patent database from the United States Patent and Trademark Office (USPTO) by extracting relevant keywords for the TFTFT system. We then preprocessed the patent data to develop a patent-International Patent Classification (IPC) matrix and a patent-keyword matrix. Next, we developed a generative topographic mapping (GTM)-based patent map using the patent-IPC matrix and detected any patent vacuums. Then, in order to confirm the promising patent vacuums, we technically examined criticality and trend analyses. Finally, we designed an IPC-based network and a keyword network with promising patent vacuums to derive a technology development strategy from a macro- and microperspective for the TFTFT system. As a result, we confirmed two promising patent vacuums. The patent vacuums found were defined as the technical field of rail vehicles suitable for TFTFT systems and the technical field of equipment and systems for freight transfer to rail vehicles. The proposed procedure and analysis method provide useful insights for developing a research and development (R&D) strategy and technology development strategy for a TFTFT system.https://www.mdpi.com/2076-3417/11/2/499truck on flatcar or truck on freight trainpatent analysispromising vacant technologypatent mapnetwork analysistime series analysis |
spellingShingle | Sungchan Jun Seong Ho Han Jiwon Yu Jumi Hwang Sangbaek Kim Chulung Lee Identification of Promising Vacant Technologies for the Development of Truck on Freight Train Transportation Systems Applied Sciences truck on flatcar or truck on freight train patent analysis promising vacant technology patent map network analysis time series analysis |
title | Identification of Promising Vacant Technologies for the Development of Truck on Freight Train Transportation Systems |
title_full | Identification of Promising Vacant Technologies for the Development of Truck on Freight Train Transportation Systems |
title_fullStr | Identification of Promising Vacant Technologies for the Development of Truck on Freight Train Transportation Systems |
title_full_unstemmed | Identification of Promising Vacant Technologies for the Development of Truck on Freight Train Transportation Systems |
title_short | Identification of Promising Vacant Technologies for the Development of Truck on Freight Train Transportation Systems |
title_sort | identification of promising vacant technologies for the development of truck on freight train transportation systems |
topic | truck on flatcar or truck on freight train patent analysis promising vacant technology patent map network analysis time series analysis |
url | https://www.mdpi.com/2076-3417/11/2/499 |
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