Identifying the Risk Factors in the Context-of-Use of Electric Kick Scooters Based on a Latent Dirichlet Allocation
Accidents related to electric kick scooters, which are widespread globally, are increasing rapidly. However, most of the research on them concentrates on reporting accident status and injury patterns. Therefore, while it is necessary to analyze safety issues from the user’s perspective, interviewing...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/23/8447 |
| _version_ | 1797546632052473856 |
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| author | Kyung-Jun Lee Chan Hyeok Yun Ilsun Rhiu Myung Hwan Yun |
| author_facet | Kyung-Jun Lee Chan Hyeok Yun Ilsun Rhiu Myung Hwan Yun |
| author_sort | Kyung-Jun Lee |
| collection | DOAJ |
| description | Accidents related to electric kick scooters, which are widespread globally, are increasing rapidly. However, most of the research on them concentrates on reporting accident status and injury patterns. Therefore, while it is necessary to analyze safety issues from the user’s perspective, interviewing or conducting a survey with those involved in an accident may not return enough data due to respondents’ memory loss. Therefore, this study aims to identify the risk factors in the context-of-use for electric kick scooters based on a topic modeling method. We collected data on risk episodes involving electric kick scooters experienced by users in their daily lives and applied text mining to analyze text responses describing the risk episodes systematically. A total of 423 risk episodes are collected from 21 electric kick scooter users in South Korea over two months from an online survey. The text responses describing risk episodes were classified into nine topics based on a latent Dirichlet allocation. From the result, four risk factors can be identified by analyzing the derived topics and the cause of the risk according to the context. Moreover, we suggested design improvement directions. This study can be helpful for designing safer electric kick scooters considering safety. |
| first_indexed | 2024-03-10T14:32:27Z |
| format | Article |
| id | doaj.art-d1ea234be7ef4321b2986401833f4be4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:32:27Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d1ea234be7ef4321b2986401833f4be42023-11-20T22:30:12ZengMDPI AGApplied Sciences2076-34172020-11-011023844710.3390/app10238447Identifying the Risk Factors in the Context-of-Use of Electric Kick Scooters Based on a Latent Dirichlet AllocationKyung-Jun Lee0Chan Hyeok Yun1Ilsun Rhiu2Myung Hwan Yun3Department of Industrial Engineering & Institute for Industrial System Innovation, Seoul National University, Seoul 08826, KoreaDepartment of Industrial Engineering & Institute for Industrial System Innovation, Seoul National University, Seoul 08826, KoreaDivision of Future Convergence (HCI Science Major), Dongduk Women’s University, Seoul 02748, KoreaDepartment of Industrial Engineering & Institute for Industrial System Innovation, Seoul National University, Seoul 08826, KoreaAccidents related to electric kick scooters, which are widespread globally, are increasing rapidly. However, most of the research on them concentrates on reporting accident status and injury patterns. Therefore, while it is necessary to analyze safety issues from the user’s perspective, interviewing or conducting a survey with those involved in an accident may not return enough data due to respondents’ memory loss. Therefore, this study aims to identify the risk factors in the context-of-use for electric kick scooters based on a topic modeling method. We collected data on risk episodes involving electric kick scooters experienced by users in their daily lives and applied text mining to analyze text responses describing the risk episodes systematically. A total of 423 risk episodes are collected from 21 electric kick scooter users in South Korea over two months from an online survey. The text responses describing risk episodes were classified into nine topics based on a latent Dirichlet allocation. From the result, four risk factors can be identified by analyzing the derived topics and the cause of the risk according to the context. Moreover, we suggested design improvement directions. This study can be helpful for designing safer electric kick scooters considering safety.https://www.mdpi.com/2076-3417/10/23/8447electric kick scootersafetycontext-of-useergonomic designtopic modeling |
| spellingShingle | Kyung-Jun Lee Chan Hyeok Yun Ilsun Rhiu Myung Hwan Yun Identifying the Risk Factors in the Context-of-Use of Electric Kick Scooters Based on a Latent Dirichlet Allocation Applied Sciences electric kick scooter safety context-of-use ergonomic design topic modeling |
| title | Identifying the Risk Factors in the Context-of-Use of Electric Kick Scooters Based on a Latent Dirichlet Allocation |
| title_full | Identifying the Risk Factors in the Context-of-Use of Electric Kick Scooters Based on a Latent Dirichlet Allocation |
| title_fullStr | Identifying the Risk Factors in the Context-of-Use of Electric Kick Scooters Based on a Latent Dirichlet Allocation |
| title_full_unstemmed | Identifying the Risk Factors in the Context-of-Use of Electric Kick Scooters Based on a Latent Dirichlet Allocation |
| title_short | Identifying the Risk Factors in the Context-of-Use of Electric Kick Scooters Based on a Latent Dirichlet Allocation |
| title_sort | identifying the risk factors in the context of use of electric kick scooters based on a latent dirichlet allocation |
| topic | electric kick scooter safety context-of-use ergonomic design topic modeling |
| url | https://www.mdpi.com/2076-3417/10/23/8447 |
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