Identifying the Indoor Space Characteristics of an Urban Railway Station Based on Pedestrian Trajectory Data
Stations are being converted into various living spaces that can be used for public transportation, work, commerce, and leisure. To satisfy the various requirements and expectations for functional extension, it is necessary to investigate and understand the phenomena caused by users. A methodology t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/8401318 |
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| _version_ | 1832555379717832704 |
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| author | Eunbi Jeong Soyoung Iris You Jun Lee Daeseop Moon |
| author_facet | Eunbi Jeong Soyoung Iris You Jun Lee Daeseop Moon |
| author_sort | Eunbi Jeong |
| collection | DOAJ |
| description | Stations are being converted into various living spaces that can be used for public transportation, work, commerce, and leisure. To satisfy the various requirements and expectations for functional extension, it is necessary to investigate and understand the phenomena caused by users. A methodology to cluster the characteristics of pedestrian space of a railway station through the pedestrian trajectory data collected from an actual operating station is proposed in this paper. Then the spatial usability of the movement and stay of pedestrians were defined through the results of the clustering. The procedure to cluster the indoor space characteristics of an urban railway station in this study consists of four steps: data collection, feature vector extraction, K-means clustering, and cluster characteristics analysis. A case study was conducted for the Samseong station. The results of the proposed spatial clustering analysis showed that there are several types of spaces depending on the space occupancy characteristics of pedestrians. The proposed methodology could be applied to indoor space diagnosis from the perspective of station monitoring and management. In addition, the station operator could respond flexibly to unexpected events by monitoring the indoor spaces according to whether the flow is normal or suggestive of an emergency. |
| format | Article |
| id | doaj-art-54d769c51b0e4fa5a08cbe5ef166d266 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-54d769c51b0e4fa5a08cbe5ef166d2662025-02-03T05:48:21ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/84013188401318Identifying the Indoor Space Characteristics of an Urban Railway Station Based on Pedestrian Trajectory DataEunbi Jeong0Soyoung Iris You1Jun Lee2Daeseop Moon3Innovative Transport Policy Division, Korea Railroad Research Institute, Uiwang-si 16105, Republic of KoreaInnovative Transport Policy Division, Korea Railroad Research Institute, Uiwang-si 16105, Republic of KoreaInnovative Transport Policy Division, Korea Railroad Research Institute, Uiwang-si 16105, Republic of KoreaInnovative Transport Policy Division, Korea Railroad Research Institute, Uiwang-si 16105, Republic of KoreaStations are being converted into various living spaces that can be used for public transportation, work, commerce, and leisure. To satisfy the various requirements and expectations for functional extension, it is necessary to investigate and understand the phenomena caused by users. A methodology to cluster the characteristics of pedestrian space of a railway station through the pedestrian trajectory data collected from an actual operating station is proposed in this paper. Then the spatial usability of the movement and stay of pedestrians were defined through the results of the clustering. The procedure to cluster the indoor space characteristics of an urban railway station in this study consists of four steps: data collection, feature vector extraction, K-means clustering, and cluster characteristics analysis. A case study was conducted for the Samseong station. The results of the proposed spatial clustering analysis showed that there are several types of spaces depending on the space occupancy characteristics of pedestrians. The proposed methodology could be applied to indoor space diagnosis from the perspective of station monitoring and management. In addition, the station operator could respond flexibly to unexpected events by monitoring the indoor spaces according to whether the flow is normal or suggestive of an emergency.http://dx.doi.org/10.1155/2019/8401318 |
| spellingShingle | Eunbi Jeong Soyoung Iris You Jun Lee Daeseop Moon Identifying the Indoor Space Characteristics of an Urban Railway Station Based on Pedestrian Trajectory Data Journal of Advanced Transportation |
| title | Identifying the Indoor Space Characteristics of an Urban Railway Station Based on Pedestrian Trajectory Data |
| title_full | Identifying the Indoor Space Characteristics of an Urban Railway Station Based on Pedestrian Trajectory Data |
| title_fullStr | Identifying the Indoor Space Characteristics of an Urban Railway Station Based on Pedestrian Trajectory Data |
| title_full_unstemmed | Identifying the Indoor Space Characteristics of an Urban Railway Station Based on Pedestrian Trajectory Data |
| title_short | Identifying the Indoor Space Characteristics of an Urban Railway Station Based on Pedestrian Trajectory Data |
| title_sort | identifying the indoor space characteristics of an urban railway station based on pedestrian trajectory data |
| url | http://dx.doi.org/10.1155/2019/8401318 |
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