Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain
The burgeoning dockless bike-sharing system presents a promising solution to the first- and last-mile transportation challenge by connecting trip origins/destinations to metro stations. However, the differentiation between metro passengers and DBS riders, as they belong to distinct systems, hinders...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/4540251 |
| _version_ | 1797247991745085440 |
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| author | Xiang Li Qipeng Yan Yixiong Tang Chen Luo |
| author_facet | Xiang Li Qipeng Yan Yixiong Tang Chen Luo |
| author_sort | Xiang Li |
| collection | DOAJ |
| description | The burgeoning dockless bike-sharing system presents a promising solution to the first- and last-mile transportation challenge by connecting trip origins/destinations to metro stations. However, the differentiation between metro passengers and DBS riders, as they belong to distinct systems, hinders the precise identification of DBS-metro transfers. This study introduces an innovative method employing mobility chains to establish spatiotemporal relationships, including spatiotemporal conflicts and similarities, among potential users from both systems. This significantly enhances the precision of user matching. An empirical study in Chengdu validates the method’s increased accuracy and examines travel patterns, yielding the following insights: (1) Introduction of the mobility chain reduces average matched pairs by 28.27% and improves accuracy by 18.36%. The addition of spatial-temporal similarity further boosts accuracy by 19.32%. (2) Median distances for DBS-metro access and egress transfers are approximately 950 meters. Short trips of 650–750 meters are prevalent, while trips exceeding 1.5 kilometers lead passengers to opt for alternative modes. (3) Temporal patterns reveal weekday peaks at 8:00, 9:00, and 17:00. On weekends, transfers are uniformly distributed, mainly within urban areas. Suburban stations exhibit reduced weekend activity. These findings can provide valuable insights for enhancing DBS bicycle redistribution, promoting transportation mode integration, and fostering urban transportation’s sustainable development. |
| first_indexed | 2024-04-24T20:07:29Z |
| format | Article |
| id | doaj.art-2070365da172466696d5d8ff32f171de |
| institution | Directory Open Access Journal |
| issn | 2042-3195 |
| language | English |
| last_indexed | 2024-04-24T20:07:29Z |
| publishDate | 2024-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj.art-2070365da172466696d5d8ff32f171de2024-03-24T00:00:02ZengHindawi-WileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/4540251Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility ChainXiang Li0Qipeng Yan1Yixiong Tang2Chen Luo3School of Transportation and LogisticsSchool of Transportation and LogisticsSchool of Transportation and LogisticsCollege of Air Traffic ManagementThe burgeoning dockless bike-sharing system presents a promising solution to the first- and last-mile transportation challenge by connecting trip origins/destinations to metro stations. However, the differentiation between metro passengers and DBS riders, as they belong to distinct systems, hinders the precise identification of DBS-metro transfers. This study introduces an innovative method employing mobility chains to establish spatiotemporal relationships, including spatiotemporal conflicts and similarities, among potential users from both systems. This significantly enhances the precision of user matching. An empirical study in Chengdu validates the method’s increased accuracy and examines travel patterns, yielding the following insights: (1) Introduction of the mobility chain reduces average matched pairs by 28.27% and improves accuracy by 18.36%. The addition of spatial-temporal similarity further boosts accuracy by 19.32%. (2) Median distances for DBS-metro access and egress transfers are approximately 950 meters. Short trips of 650–750 meters are prevalent, while trips exceeding 1.5 kilometers lead passengers to opt for alternative modes. (3) Temporal patterns reveal weekday peaks at 8:00, 9:00, and 17:00. On weekends, transfers are uniformly distributed, mainly within urban areas. Suburban stations exhibit reduced weekend activity. These findings can provide valuable insights for enhancing DBS bicycle redistribution, promoting transportation mode integration, and fostering urban transportation’s sustainable development.http://dx.doi.org/10.1155/2024/4540251 |
| spellingShingle | Xiang Li Qipeng Yan Yixiong Tang Chen Luo Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain Journal of Advanced Transportation |
| title | Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain |
| title_full | Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain |
| title_fullStr | Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain |
| title_full_unstemmed | Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain |
| title_short | Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain |
| title_sort | identification dockless bike sharing and metro transfer travelers through mobility chain |
| url | http://dx.doi.org/10.1155/2024/4540251 |
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