| id |
caadria2024_199 |
| authors |
Huang, Yulu, Song, Qiwei and Qiu, Waishan |
| year |
2024 |
| title |
Do Visually Perceived Design Qualities Influence Dockless Bikeshare Cycling Routes? A Case Study of Ithaca Using GPS Trajectories |
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 99–108 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.2.099
|
| summary |
Understanding the influences of the built environment (BE) characteristics on the dockless bike-sharing system (DBS) is crucial for supporting and developing sustainable transportation mode. Previous studies on DBS cycling have primarily investigated the effects of macro-level BE characteristics (e.g., land use) or limited street features (e.g., greenery), overlooking that of perceived street design qualities such as enclosure. To better understand whether and how street-level environment characteristics, especially perceived street design qualities, influence DBS cycling routes, we calculate cycling volume based on GPS trajectories in Ithaca, a small town in New York State, and then quantify visual features and perceived design qualities using street view imagery (SVI) and computer vision (CV). Our analysis, employing linear regression and spatial regression models while controlling macro-environmental attributes as covariates, reveal the significant association between perceived design qualities and DBS cycling trip volume, confirming the significance of considering design qualities in DBS cycling studies. Geographically Weighted Regression (GWR) model explains the spatially heterogeneous effects of street-level attributes, offering practical suggestions for informing spatially varying policies and interventions for creating a cycling-friendly environment. |
| keywords |
dockless bikeshare, street-level characteristics, urban design quality, street view imagery, semantic segmentation |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (713,779 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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