A Trip Purpose Inference Method Considering the Origin and Destination of Shared Bicycles
This study advances the inference of travel purposes for dockless bike-sharing users by integrating dockless bike-sharing and point of interest (POI) data, thereby enhancing traditional models. The methodology involves cleansing dockless bike-sharing datasets, identifying destination areas via users...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/483 |
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| author | Haicheng Xiao Xueyan Shen Xiujian Yang |
| author_facet | Haicheng Xiao Xueyan Shen Xiujian Yang |
| author_sort | Haicheng Xiao |
| collection | DOAJ |
| description | This study advances the inference of travel purposes for dockless bike-sharing users by integrating dockless bike-sharing and point of interest (POI) data, thereby enhancing traditional models. The methodology involves cleansing dockless bike-sharing datasets, identifying destination areas via users’ walking radii from their start and end points, and categorizing POI data to establish a correlation between trip purposes and POI types. The innovative GMOD model (gravity model considering origin and destination) is developed by modifying the basic gravity model parameters with the distribution of POI types and travel time. This refined approach significantly improves the accuracy of predicting travel purposes, surpassing standard gravity models. Particularly effective in identifying less frequent but critical purposes such as transfers, medical visits, and educational trips, the GMOD model demonstrates substantial improvements in these areas. The model’s efficacy in sample data tests highlights its potential as a valuable tool for urban transport analysis and in conducting comprehensive trip surveys. |
| format | Article |
| id | doaj-art-a595d247c6554cb8a259dff0e07ce516 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a595d247c6554cb8a259dff0e07ce5162025-01-10T13:15:43ZengMDPI AGApplied Sciences2076-34172025-01-0115148310.3390/app15010483A Trip Purpose Inference Method Considering the Origin and Destination of Shared BicyclesHaicheng Xiao0Xueyan Shen1Xiujian Yang2Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaThis study advances the inference of travel purposes for dockless bike-sharing users by integrating dockless bike-sharing and point of interest (POI) data, thereby enhancing traditional models. The methodology involves cleansing dockless bike-sharing datasets, identifying destination areas via users’ walking radii from their start and end points, and categorizing POI data to establish a correlation between trip purposes and POI types. The innovative GMOD model (gravity model considering origin and destination) is developed by modifying the basic gravity model parameters with the distribution of POI types and travel time. This refined approach significantly improves the accuracy of predicting travel purposes, surpassing standard gravity models. Particularly effective in identifying less frequent but critical purposes such as transfers, medical visits, and educational trips, the GMOD model demonstrates substantial improvements in these areas. The model’s efficacy in sample data tests highlights its potential as a valuable tool for urban transport analysis and in conducting comprehensive trip surveys.https://www.mdpi.com/2076-3417/15/1/483destination inferenceshared bicyclesgravity modelurban transportationpoint of interest—POI |
| spellingShingle | Haicheng Xiao Xueyan Shen Xiujian Yang A Trip Purpose Inference Method Considering the Origin and Destination of Shared Bicycles Applied Sciences destination inference shared bicycles gravity model urban transportation point of interest—POI |
| title | A Trip Purpose Inference Method Considering the Origin and Destination of Shared Bicycles |
| title_full | A Trip Purpose Inference Method Considering the Origin and Destination of Shared Bicycles |
| title_fullStr | A Trip Purpose Inference Method Considering the Origin and Destination of Shared Bicycles |
| title_full_unstemmed | A Trip Purpose Inference Method Considering the Origin and Destination of Shared Bicycles |
| title_short | A Trip Purpose Inference Method Considering the Origin and Destination of Shared Bicycles |
| title_sort | trip purpose inference method considering the origin and destination of shared bicycles |
| topic | destination inference shared bicycles gravity model urban transportation point of interest—POI |
| url | https://www.mdpi.com/2076-3417/15/1/483 |
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