Predicting and Optimizing Restorativeness in Campus Pedestrian Spaces based on Vision Using Machine Learning and Deep Learning
Abstract
:1. Introduction
2. Mterials and Methods
2.1. Study Design and Restoration States Assessment
2.1.1. Study Design
2.1.2. Restoration State Assessment Method
2.2. Selection of Restorative Factors in Campus Pedestrian Spaces
2.2.1. SD Questionnaire
2.2.2. Eye-Tracking Experiment
Eye-Tracking Experiment in Real Scene
VR Eye-Tracking Experiment in VR
2.3. Restorative Orthogonal Experiment
2.3.1. VR Scenes Setting
2.3.2. Experimental Procedure
2.3.3. Analysis of Influence Mechanism
2.4. Training of Predictive Models
2.4.1. Decision Tree (DT)
2.4.2. k-Nearest Neighbor (KNN)
2.4.3. Artificial Neural Network (ANN)
2.4.4. Convolutional Neural Network (CNN)
2.5. Settings of Restorative Optimization
3. Results
3.1. Selection of Influencing Factors in Campus Pedestrian Spaces
3.1.1. Restorative Factors Correlation Analysis
3.1.2. Eye-Tracking Experiment Analysis
3.2. Restorative Factors Influence Mechanism
3.3. Prediction of Restorativeness
3.3.1. Machine Learning
3.3.2. Deep Learning
3.4. Optimization of Restorativeness
4. Discussion
4.1. Restorative Environmental Factors in Campus Pedestrian Spaces
4.2. Restorative Influencing Mechanism
4.3. Machine Learning and Deep Learning for Restorativeness Prediction
4.4. Implications for Restorative Design in University Campuses
5. Conclusions
5.1. Restorative Environmental Factors Selection
5.2. Interrelation of Restorativeness and Environmental Factors
5.3. Restorative Prediction of Campus Pedestrian Space
5.4. Restorative Optimization of Campus Pedestrian Space
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Restorative State Scale |
---|
1. My mind is not invaded by stressful thoughts. |
2. I can take time out from a busy life. |
3. I can lose all sense of time. |
4. I am thinking about everything and nothing at the same time. |
5. I can make space to think about my problems. |
6. I can leave all my problems behind me. |
7. My mind just wanders in infinity. |
8. I can imagine myself as part of the larger cyclical process of living. |
9. I feel connected to the natural world. |
Spatial Factors | Factor of Neutrality | Adjective Pairs |
---|---|---|
Buildings and trees | Height | Short–Tall |
Buildings | Height difference | Small–Large |
Buildings and ground | Distance | Near–Far |
Buildings | Angle | Small–Large |
Buildings and ground | Color–Hue | Cold–Warm |
Buildings and ground | Color-Contrast | Low–High |
Shrubs | Area in vision | Little–Much |
Ground | Texture | Sparse–Dense |
Sky | Openness | Small–Large |
Buildings and ground | Material | Artificial–Natural |
Case | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Location | HIT 1 | HIT | HEU 2 | HNU 3 | HNU |
Type | Square | Street | Square | Path | Sidewalk |
Feature | More artificial | More natural | More artificial | More natural | More natural |
Picture of Vegetation period | |||||
Picture of Frost period |
Environmental Factors | Parameters in Orthogonal Experiment |
---|---|
Building height difference | 0, 12, 24, 36, 48, and 60 m |
Tree height | 8, 12, 16, 20, 24, and 28 m |
Shrub area | 0%, 0.03%, 0.06%, 0.09%, 0.12%, and 0.15% |
Ground texture | 100 × 100, 200 × 100, 300 × 300, 600 × 300 mm2, 600 × 600, and 1000 × 1000 mm2 |
Ground hue | 0°, 72°, 144°, 216°, 288°, and 360° |
Solution Set 1 | Solution Set 2 | … | Solution Set 20 | Thresholds | |
---|---|---|---|---|---|
HDB (m) | 27.543 | 20.819 | … | 26.382 | 20–28 |
HT (m) | 8.813 | 15. 368 | … | 12.792 | 8.5~15.5 |
AS (%) | 0.147 | 0.129 | … | 0.135 | 0.12~0.15 |
TG (m2) | 0.347 | 0.496 | … | 0.265 | 0.20~0.50 |
HG (°) | 236.597 | 245.013 | … | 146.823 | 140~245 |
ΔS (S3 − S2) | 4 (4) | 4 (3.9) | … | 4 (4) | 4 (predict) |
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Huang, K.; Wang, T.; Li, X.; Zhang, R.; Dong, Y. Predicting and Optimizing Restorativeness in Campus Pedestrian Spaces based on Vision Using Machine Learning and Deep Learning. Land 2024, 13, 1308. https://doi.org/10.3390/land13081308
Huang K, Wang T, Li X, Zhang R, Dong Y. Predicting and Optimizing Restorativeness in Campus Pedestrian Spaces based on Vision Using Machine Learning and Deep Learning. Land. 2024; 13(8):1308. https://doi.org/10.3390/land13081308
Chicago/Turabian StyleHuang, Kuntong, Taiyang Wang, Xueshun Li, Ruinan Zhang, and Yu Dong. 2024. "Predicting and Optimizing Restorativeness in Campus Pedestrian Spaces based on Vision Using Machine Learning and Deep Learning" Land 13, no. 8: 1308. https://doi.org/10.3390/land13081308