| id |
caadria2021_191 |
| authors |
Shou, Xinyue, Chen, Pinyang and Zheng, Hao |
| year |
2021 |
| title |
Predicting the Heat Map of Street Vendors from Pedestrian Flow through Machine Learning |
| source |
A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 569-578 |
| doi |
https://doi.org/10.52842/conf.caadria.2021.2.569
|
| summary |
Street vending is a recent policy advocated by city governments to support small and intermediate businesses in the post-pandemic period in China. Street vendors select their locations primarily based on their intuitions about the surrounding environment; they temporarily occupy popular locations that benefit their business. Taking the city of Chengdu as an example, this study aims to formulate the rules governing vendors location selection using machine learning and big data analysis techniques, thus identifying streets likely to become vital street markets. We propose a semantic segmentation method to construct heat maps that visualize and quantify the distribution of street vendors and pedestrians on public urban streets. The image-based generative adversarial network (GAN) is then trained to predict the vendors heat maps from the pedestrians heat map, finding the relationship between the locations of the vendors and the pedestrians. Our successful prediction of the vendors locations highlights machine learning techniques ability to quantify experience-based decision strategies. Moreover, suggesting potential marketing locations to vendors could help increase cities vitality. |
| keywords |
Machine Learning; Big Data Analysis; Semantic Segmentation; Generative Adversarial Networks |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (14,090,662 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:56 |
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