A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction
Abstract
:1. Introduction
2. Literature Review
2.1. Streetscapes and Walkability
2.2. Machine-Learning Model and Explainable AI
2.3. Limitation of Previous Studies and Differences in This Study
3. Materials and Methods
3.1. Materials
3.1.1. Study Area
3.1.2. Streetscapes Images and the Naver Street View API
3.2. Methods
3.2.1. Semantic Segmentation
3.2.2. Edge Detection
3.2.3. Analytic Frame
4. Variables
4.1. Enclosure
4.2. Openness
4.3. Greenery
4.4. Complexity
4.5. Area Ratio
5. Analysis
5.1. Machine-Learning Analysis Methods
5.1.1. Logistic Regression Classification
5.1.2. Random Forest Classifier
5.1.3. XGBoost Classifier
5.2. Evaluating the Machine-Learning Techniques
5.3. Interpretable Machine-Learning Techniques
6. Results
6.1. Machine-Learning Models
6.2. Interpretation of the Machine-Learning Model
6.2.1. Analysis of the Street Environment Characteristics That Affect Pedestrian Satisfaction
6.2.2. Analysis of the Relationship between Walking Satisfaction and Visual Features
7. Discussion
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Survey Point | 1000 Representative Places in Seoul | Survey Locations in Seoul |
---|---|---|
Survey Target | Pedestrians passing through the representative points | |
Survey Period | Every Friday and Saturday during October 2015 | |
Survey Range | 10 people per point per day | |
Survey Method | Individual interviews using questionnaires | |
Survey Contents | Gender, age, occupation, residence, purpose, frequency of visits, overall satisfaction with the walking environment |
Factor | Element | Item | Mean/Proportion | S.D. | Min. | Max. | |
---|---|---|---|---|---|---|---|
Dependent variable | Pedestrian satisfaction |
| 2.73 | 0.966 | 1 | 5 | |
Not satisfied (ref.) | 35% | - | - | - | |||
Satisfied | 65% | - | - | - | |||
Personal Characteristics | Gender | Men (ref.) | 46% | - | - | - | |
Women | 54% | - | - | - | |||
Age | 15–19 | 5% | - | - | - | ||
20–29 | 19% | - | - | - | |||
30–39 | 18% | - | - | - | |||
40–49 | 17% | - | - | - | |||
50–59 | 23% | - | - | - | |||
60+ | 18% | - | - | - | |||
Frequency of passage | First time | 4% | - | - | - | ||
Every day | 30% | - | - | - | |||
1–2 times a week | 19% | - | - | - | |||
3–5 times a week | 33% | - | - | - | |||
Less than twice a month | 14% | - | - | - | |||
Purpose of visit | Commute | 22% | - | - | - | ||
Personal | 37% | - | - | - | |||
Work or study | 19% | - | - | - | |||
Leisure | 14% | - | - | - | |||
Passage | 8% | - | - | - | |||
Job | Student | 14% | - | - | - | ||
Housewife | 22% | - | - | - | |||
White-collar | 27% | - | - | - | |||
Blue-collar | 7% | - | - | - | |||
Sales and service | 11% | - | - | - | |||
Self-employment | 11% | - | - | - | |||
Other | 8% | - | - | - | |||
Physical features | Width of the sidewalk | Width of the sidewalk (m) | 4.12 | 2.312 | 1 | 24 | |
The number of lanes | The number of lanes | 3.93 | 2.637 | 1 | 18 | ||
Bus road | Bus road | No (ref.) | 30% | - | - | - | |
Yes | 70% | - | - | - | |||
Centerline | Centerline | No (ref.) | 29% | - | - | - | |
Yes | 71% | - | - | - | |||
Street furniture | Street furniture | No (ref.) | 8% | - | - | - | |
Yes | 92% | - | - | - | |||
Road type | Car only | 7% | - | - | - | ||
Pedestrian only | 70% | - | - | - | |||
Mixed-use | 23% | - | - | - | |||
Braille block | Braille block | No (ref.) | 64% | - | - | - | |
Yes | 36% | - | - | - | |||
Slope | Slope | No (ref.) | 77% | - | - | - | |
Yes | 23% | - | - | - | |||
Fence | Pedestrian safety fence | No (ref.) | 79% | - | - | - | |
Yes | 21% | - | - | - | |||
Bus stop | Bus stop | No (ref.) | 64% | - | - | - | |
Yes | 36% | - | - | - | |||
Subway station | Subway station | No (ref.) | 65% | - | - | - | |
Yes | 35% | - | - | - | |||
Crosswalk | Crosswalk | No (ref.) | 39% | - | - | - | |
Yes | 61% | - | - | - | |||
Land use | Commercial area | 18% | - | - | - | ||
Green area | 3% | - | - | - | |||
Semi-residential area | 4% | - | - | - | |||
Semi-industrial area | 5% | - | - | - | |||
Class Ⅰ residential area | 6% | - | - | - | |||
Class Ⅱ residential area | 30% | - | - | - | |||
Class Ⅲ residential area | 33% | - | - | - | |||
Visual features | Urban design qualities | Enclosure | Sum of the area ratios of buildings and street trees | 0.549 | 0.153 | 0.145 | 0.912 |
Openness | The area ratio of the sky | 0.208 | 0.096 | 0 | 0.539 | ||
Greenery | Sum of planting area ratios | 0.208 | 0.096 | 0 | 0.539 | ||
Complexity | Amount of visual information | 1.283 | 0.238 | 0.516 | 2.249 | ||
Area ratio | The proportion of buildings | 0.394 | 0.212 | 0.002 | 0.895 | ||
The proportion of the road | 0.144 | 0.060 | 0 | 0.273 | |||
The proportion of the sidewalk | 0.038 | 0.031 | 0 | 0.287 | |||
The proportion of the street furniture | 0.013 | 0.018 | 0 | 0.160 |
Model | Logistic Regression | Random Forest | XGBoost |
---|---|---|---|
Accuracy | 0.65 | 0.72 | 0.82 |
Precision | 0.60 | 0.76 | 0.83 |
Recall | 0.51 | 0.81 | 0.92 |
F1 score | 0.53 | 0.79 | 0.87 |
AUC score | 0.56 | 0.76 | 0.90 |
ROC curve |
Variable | SHAP Value | Variable | SHAP Value |
---|---|---|---|
The proportion of the road | 0.1551 | Semi industrial area | 0.0493 |
The proportion of the sidewalk | 0.1475 | Class Ⅱ residential area | 0.0409 |
The proportion of the street furniture | 0.1412 | Purpose of passage | 0.0356 |
Enclosure | 0.1399 | Purpose of commute | 0.0316 |
Complexity | 0.1311 | Visit every day | 0.0315 |
Greenery | 0.1252 | Pedestrian-only road | 0.0276 |
Proportion of buildings | 0.1157 | Slope | 0.0266 |
Openness | 0.0949 | Crosswalk | 0.0247 |
Width of the sidewalk | 0.0792 | Student | 0.0236 |
The number of lanes | 0.0636 | Visit 3–5 times a week | 0.0255 |
The Proportion of the Road | The Proportion of the Sidewalk | Complexity | Greenery |
---|---|---|---|
The Proportion of Street Furniture | Enclosure | The Proportion of Buildings | Openness |
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Lee, J.; Kim, D.; Park, J. A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction. Sustainability 2022, 14, 5730. https://doi.org/10.3390/su14095730
Lee J, Kim D, Park J. A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction. Sustainability. 2022; 14(9):5730. https://doi.org/10.3390/su14095730
Chicago/Turabian StyleLee, Jiyun, Donghyun Kim, and Jina Park. 2022. "A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction" Sustainability 14, no. 9: 5730. https://doi.org/10.3390/su14095730