| id |
acadia20_160 |
| authors |
Sun, Yunjuan; Jiang, Lei; Zheng, Hao |
| year |
2020 |
| title |
A Machine Learning Method of Predicting Behavior Vitality Using Open Source Data |
| source |
ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 160-168. |
| doi |
https://doi.org/10.52842/conf.acadia.2020.2.160
|
| summary |
The growing popularity of machine learning has provided new opportunities to predict certain behaviors precisely by utilizing big data. In this research, we use an image-based neural network to explore the relationship between the built environment and the activity of bicyclists in that environment. The generative model can produce heat maps that can be used to predict quantitatively the cycling and running activity in a given area, and then use urban design to enhance urban vitality in that area. In the machine learning model, the input image is a plan view of the built environment, and the output image is a heat map showing certain activities in the corresponding area. After it is trained, the model yields output (the predicted heat map) at an acceptable level of accuracy. The heat map shows the levels and conditions of the subject activity in different sections of the built environment. Thus, the predicted results can help identify where regional vitality can be improved. Using this method, designers can not only predict the behavioral heat distribution but also examine the different interactions between behaviors and aspects of the environment. The extent to which factors might influence behaviors is also studied by generating a heat map of the modified plan. In addition to the potential applications of this approach, its limitations and areas for improvement are also proposed. |
| series |
ACADIA |
| type |
paper |
| email |
|
| full text |
 file.pdf (7,081,618 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/10/22 12:06 |
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