Deep Learning-Based Systems for Evaluating and Enhancing Child-Friendliness of Urban Streets—A Case of Shanghai Urban Street
Abstract
1. Introduction
2. Literature Review
2.1. Research on Child-Friendliness of Urban Streets
- (1)
- The Relationship Between Physical Spatial Attributes and Children’s Behavior
- (2)
- Children’s Subjective Perception and Preferences
- (3)
- Institutional Mechanisms and Spatial Governance
2.2. Application of Deep Learning in Urban Studies
- (1)
- Automated Recognition and Semantic Interpretation of Urban Street Scenes
- (2)
- Perceptual Dimension Modeling and Multimodal Fusion Analysis
- (3)
- Topological Modeling of Spatial Structure and Behavioral Patterns
2.3. Research Gaps
3. Methodology
3.1. Design of Urban Street Spatial Features
3.2. Design of Child-Friendliness Evaluation Indicators
3.3. Design of Child-Friendliness Improvement Needs
3.4. Framework Design and Evaluation Workflow
4. Automated Evaluation and Enhancement Models
4.1. Data Collection and Preprocessing
4.2. Design and Performance Evaluation of the Evaluation Model
4.3. Design and Performance Evaluation of the Enhancement Model
5. Practical Application
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Determining Decision Weights of Entities Algorithm (Analytic Hierarchy Process)
Appendix B. Evaluation Model Dataset Augmentation Algorithm
Appendix C. Enhancement Model Dataset Augmentation Algorithm
Appendix D. Urban Street Child-Friendliness Evaluation Model Algorithm
Appendix E. Urban Street Child-Friendliness Enhancement Model Algorithm
References
- Nasrabadi, M.T.; García, E.H.; Pourzakarya, M. Let children plan neighborhoods for a sustainable future: A sustainable child-friendly city approach. Local Environ. 2021, 26, 198–215. [Google Scholar] [CrossRef]
- Zhao, J.; Su, W.; Luo, J.C.; Zuo, J. Evaluation and Optimization of Walkability of Children’s School Travel Road for Accessibility and Safety Improvement. Int. J. Environ. Res. Public Health 2022, 19, 71. [Google Scholar] [CrossRef]
- Rigolon, A.; Toker, Z.; Gasparian, N. Who has more walkable routes to parks? An environmental justice study of Safe Routes to Parks in neighborhoods of Los Angeles. J. Urban Aff. 2018, 40, 576–591. [Google Scholar] [CrossRef]
- Brussoni, M.; Lin, Y.Y.; Han, C.; Janssen, I.; Schuurman, N.; Boyes, R.; Swanlund, D.; Mâsse, L.C. A qualitative investigation of unsupervised outdoor activities for 10-to 13-year-old children: “I like adventuring but I don’t like adventuring without being careful”. J. Environ. Psychol. 2020, 70, 101460. [Google Scholar] [CrossRef]
- D’Haese, S.; Vanwolleghem, G.; Hinckson, E.; De Bourdeaudhuij, I.; Deforche, B.; Van Dyck, D.; Cardon, G. Cross-continental comparison of the association between the physical environment and active transportation in children: A systematic review. Int. J. Behav. Nutr. Phy. 2015, 12, 145. [Google Scholar] [CrossRef]
- Ramezani, S.; Said, I. Children’s nomination of friendly places in an urban neighbourhood in Shiraz, Iran. Child. Geogr. 2013, 11, 7–27. [Google Scholar] [CrossRef]
- Murota, M. A study on the Use of Parks in the Green Matrix System of Kohoku New Town, Japan: -Focusing on Parks Combined with Pedestrian Roads-. J. Asian Archit. Build. Eng. 2009, 8, 73–79. [Google Scholar] [CrossRef]
- Poon, B.T.; Atchison, C.; Kwan, A. Understanding the Influence of Community-Level Determinants on Children’s Social and Emotional Well-Being: A Systems Science and Participatory Approach. Int. J. Environ. Res. Public Health 2022, 19, 5972. [Google Scholar] [CrossRef]
- Zhang, F.; Salazar-Miranda, A.; Duarte, F.; Vale, L.; Hack, G.; Chen, M.; Liu, Y.; Batty, M.; Ratti, C. Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery. Ann. Am. Assoc. Geogr. 2024, 114, 876–897. [Google Scholar] [CrossRef]
- Schmidhuber, J. Deep learning in neural networks: An overview. Neural Netw. 2015, 61, 85–117. [Google Scholar] [CrossRef]
- Dai, S.Q.; Li, Y.C.; Stein, A.; Yang, S.J.; Jia, P. Street view imagery-based built environment auditing tools: A systematic review. Int. J. Geogr. Inf. Sci. 2024, 38, 1136–1157. [Google Scholar] [CrossRef]
- Wang, R.; Liu, J. Research on ecological-cultural coupling evaluation and optimization strategy of urban ecological parks: A case study of Yubei District, Chongqing. J. Hum. Settl. West China 2024, 39, 157–164. [Google Scholar]
- Giles-Corti, B.; Wood, G.; Pikora, T.; Learnihan, V.; Bulsara, M.; Van Niel, K.; Timperio, A.; McCormack, G.; Villanueva, K. School site and the potential to walk to school: The impact of street connectivity and traffic exposure in school neighborhoods. Health Place 2011, 17, 545–550. [Google Scholar] [CrossRef]
- Zhang, R.Y.; Huang, Q.; Peng, Z.M.; Zhang, X.Y.; Shang, L.; Yang, C.L. Evaluating the Impact of Elementary School Urban Neighborhood Color on Children’s Mentalization of Emotions through Multi-Source Data. Buildings 2024, 14, 3128. [Google Scholar] [CrossRef]
- Pitsikali, A.; Parnell, R.; McIntyre, L. The public value of child-friendly space Reconceptualising the playground. Archnet-IJAR 2020, 14, 149–165. [Google Scholar] [CrossRef]
- Torres, J.; Cloutier, M.S.; Bergeron, J.; St-Denis, A. ‘They installed a speed bump’: Children’s perceptions of traffic-calming measures around elementary schools. Child. Geogr. 2020, 18, 477–489. [Google Scholar] [CrossRef]
- Jin, S.; Deng, Y.; Tu, H. Reprogramming Urban Communities: Enhancing Children’s Well-being through a Value-Based Programming Approach in Inclusive Planning and Design. In Proceedings of the 60th ISOCARP World Planning Congress, Siena, Italy, 8–12 October 2024. [Google Scholar]
- Wang, Y.; Sun, Y.A.; Sun, Y.H.; He, T. Unveiling the magic of mega-city block environments: Investigating the intriguing mechanisms shaping children’s spontaneous play preferences. Front. Psychol. 2024, 15, 1354236. [Google Scholar] [CrossRef]
- Sheng, K.Y.; Liu, L.; Wang, F.; Li, S.N.; Zhou, X. An Eye-Tracking Study on Exploring Children’s Visual Attention to Streetscape Elements. Buildings 2025, 15, 605. [Google Scholar] [CrossRef]
- Yin, B.; Wang, L.J.; Xu, Y.; Heng, K.C. A Study on the Spatial Perception and Inclusive Characteristics of Outdoor Activity Spaces in Residential Areas for Diverse Populations from the Perspective of All-Age Friendly Design. Buildings 2025, 15, 895. [Google Scholar] [CrossRef]
- Moore, K.A. Developing an Indicator System to Measure Child Well-Being: Lessons Learned over Time. Child Indic. Res. 2020, 13, 729–739. [Google Scholar] [CrossRef]
- Schepers, S.; Schoffelen, J.; Zaman, B.; Dreessen, K. ‘I’m the boss of the Stiemerbeek valley!’ Reconsidering children’s empowerment in participatory design from the perspective of infrastructuring. Codesign 2022, 18, 340–354. [Google Scholar] [CrossRef]
- Cordero-Vinueza, V.A.; van Dijk, T.; Lamker, C.; Niekerk, F. Making child-friendly cities: A quest for local agenda-setting. Cities 2025, 161, 105882. [Google Scholar] [CrossRef]
- von Stülpnagel, R.; Riach, N.; Hologa, R.; Kees, J.; Gössling, S. School route safety perceptions of primary school children and their parents: Effects of transportation mode and infrastructure. Int. J. Sustain. Transp. 2024, 18, 465–477. [Google Scholar] [CrossRef]
- Wang, X.; Huang, J.P.; Qin, Z.Y.; Gan, W.; He, Z.; Li, X. Is the Children’s 15-Minute City an Effective Framework for Enhancing Children’s Health and Well-Being? An Empirical Analysis from Western China. Buildings 2025, 15, 248. [Google Scholar] [CrossRef]
- Li, F.; Yigitcanlar, T.; Nepal, M.; Nguyen, K.; Dur, F. Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework. Sustain. Cities Soc. 2023, 96, 104653. [Google Scholar] [CrossRef]
- Khoei, T.T.; Slimane, H.O.; Kaabouch, N. Deep learning: Systematic review, models, challenges, and research directions. Neural Comput. Appl. 2023, 35, 23103–23124. [Google Scholar] [CrossRef]
- Tang, F.L.; Zeng, P.; Wang, L.; Zhang, L.H.; Xu, W.X. Urban Perception Evaluation and Street Refinement Governance Supported by Street View Visual Elements Analysis. Remote Sens. 2024, 16, 3661. [Google Scholar] [CrossRef]
- Baduge, S.K.; Thilakarathna, S.; Perera, J.S.; Arashpour, M.; Sharafi, P.; Teodosio, B.; Shringi, A.; Mendis, P. Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automat. Constr. 2022, 141, 104440. [Google Scholar] [CrossRef]
- Mo, Y.J.; Wu, Y.; Yang, X.N.; Liu, F.L.; Liao, Y.J. Review the state-of-the-art technologies of semantic segmentation based on deep learning. Neurocomputing 2022, 493, 626–646. [Google Scholar] [CrossRef]
- Wu, Y.F.; Liu, Q.Q.; Hang, T.; Yang, Y.H.; Wang, Y.J.; Cao, L. Integrating restorative perception into urban street planning: A framework using street view images, deep learning, and space syntax. Cities 2024, 147, 104791. [Google Scholar] [CrossRef]
- Yang, Y.H.; Wang, Q.; Wu, D.C.; Hang, T.; Ding, H.N.; Wu, Y.F.; Liu, Q.Q. Constructing child-friendly cities: Comprehensive evaluation of street-level child-friendliness using the method of empathy-based stories, street view images, and deep learning. Cities 2024, 154, 105385. [Google Scholar] [CrossRef]
- Ho, L.C.; Wei, Y.T.; Li, D.Y.; Chiang, Y.C. Revealing emotional responses to urban environmental elements through street view data and deep learning. Environ. Plan. B-Urban Anal. City Sci. 2025, 23998083251348280. [Google Scholar] [CrossRef]
- Huang, Y.J.; Zhang, F.; Gao, Y.; Tu, W.; Duarte, F.; Ratti, C.; Guo, D.S.; Liu, Y. Comprehensive urban space representation with varying numbers of street-level images. Comput. Environ. Urban Syst. 2023, 106, 102043. [Google Scholar] [CrossRef]
- Wang, S.Q.; Liang, C.; Gao, Y.F.; Ye, Y.; Qiu, J.Y.; Tao, C.; Wang, H.F. Social media insights into spatio-temporal emotional responses to COVID-19 crisis. Health Place 2024, 85, 103174. [Google Scholar] [CrossRef] [PubMed]
- Wedyan, M.; Saeidi-Rizi, F. Assessing the Impact of Urban Environments on Mental Health and Perception Using Deep Learning: A Review and Text Mining Analysis. J. Urban Health 2024, 101, 327–343. [Google Scholar] [CrossRef] [PubMed]
- Shi, H.D.; Chen, J.F.; Feng, Z.H.; Liu, T.; Sun, D.H.; Zhou, X.L. Exploring the Influence of Environmental Characteristics on Emotional Perceptions in Metro Station Spaces. Buildings 2025, 15, 310. [Google Scholar] [CrossRef]
- Ghanbari, M.; Karimi, M.; Claramunt, C.; Lagesse, C. Enhancing Urban Transportation Flow Modeling Through a Graph Neural Network-based Spatially Weighted Interaction Model: A Case Study of Chicago Taxi Data. J. Geovis. Spat. Anal. 2025, 9, 15. [Google Scholar] [CrossRef]
- Zhang, H.; Liu, H.; Kim, C. Semantic and Instance Segmentation in Coastal Urban Spatial Perception: A Multi-Task Learning Framework with an Attention Mechanism. Sustainability 2024, 16, 833. [Google Scholar] [CrossRef]
- Stark, T.; Wurm, M.; Zhu, X.X.; Taubenböck, H. Quantifying Uncertainty in Slum Detection: Advancing Transfer Learning With Limited Data in Noisy Urban Environments. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2024, 17, 4552–4565. [Google Scholar] [CrossRef]
- Yao, Y.; Liang, Z.T.; Yuan, Z.H.; Liu, P.H.; Bie, Y.P.; Zhang, J.B.; Wang, R.Y.; Wang, J.L.; Guan, Q.F. A human-machine adversarial scoring framework for urban perception assessment using street-view images. Int. J. Geogr. Inf. Sci. 2019, 33, 2363–2384. [Google Scholar] [CrossRef]
- Guo, D.; Shi, Y.; Chen, R. Environmental affordances and children’s needs: Insights from child-friendly community streets in China. Front. Archit. Res. 2023, 12, 411–422. [Google Scholar] [CrossRef]
- Tu, H.; Jin, S. A group decision-making model for architectural programming in megaprojects. Eng. Constr. Archit. Manag. 2024, 31, 342–368. [Google Scholar] [CrossRef]
- Ye, Y.; Zeng, W.; Shen, Q.M.; Zhang, X.H.; Lu, Y. The visual quality of streets: A human-centred continuous measurement based on machine learning algorithms and street view images. Environ. Plan. B-Urban Anal. City Sci. 2019, 46, 1439–1457. [Google Scholar] [CrossRef]
- Krabbe, P.F.M. Chapter 5—Constructs and Scales. In The Measurement of Health and Health Status; Krabbe, P.F.M., Ed.; Academic Press: San Diego, CA, USA, 2017; pp. 67–89. [Google Scholar]
- Shafik, N.; Mansour, Y.; Kamel, S.; Morcos, R. The Impact of the Cairo Streets Development Project on the Independent Mobility of Children: A Field Study on the Streets of Heliopolis, Egypt. Infrastructures 2021, 6, 98. [Google Scholar] [CrossRef]
- Tezel, E. Exploring parental concerns about children’s interactions in gated communities: A case study in Istanbul. Child. Geogr. 2011, 9, 425–437. [Google Scholar] [CrossRef]
- Zhang, J.; Liu, J.; Ai, Q.; Liu, Q. Comparison and Analysis of Thermal Comfort in Summer between Riverside Parks and Parks under the Urban Viaducts in Chongqing. J. Southwest Univ. Nat. Sci. Ed. 2022, 44, 197–209. [Google Scholar]
- Lu, C.Y.; Yu, C.B.; Liu, X.W. Evaluating the Quality of Children’s Active School Travel Spaces and the Mechanisms of School District Friendliness Impact Based on Multi-Source Big Data. Land 2024, 13, 1319. [Google Scholar] [CrossRef]
- Han, H.T.; Huang, J.; Jia, X.F.; Zhang, J.T.; Guo, J.W. Evaluation of route choice for walking commutes to school and street space optimization in old urban areas of China based on a child-friendly orientation: The case of the Wuyi Park area in Zhengzhou. Child. Soc. 2024, 38, 1527–1556. [Google Scholar] [CrossRef]
- Jin, S.T.; Tu, H.J.; Li, J.F.; Fang, Y.W.; Qu, Z.; Xu, F.; Liu, K.; Lin, Y.Q. Enhancing Architectural Education through Artificial Intelligence: A Case Study of an AI-Assisted Architectural Programming and Design Course. Buildings 2024, 14, 1613. [Google Scholar] [CrossRef]
- Xu, S.; Zhuang, W. Research on the Quality Criteria of Street Space Affecting Children’s Hierarchy of School Travel Needs. J. Hum. Settl. West China 2023, 38, 45–51. [Google Scholar]
Urban Street Element | Child-Friendliness-Related Feature | Feature Quantification Method |
---|---|---|
Sidewalk | Sidewalk Width | Average effective pedestrian passage width (unit: meters) |
Sidewalk Length | Straight-line distance between intersections (unit: meters) | |
Degree of Sidewalk Encroachment | The ratio of encroached sidewalk length to total sidewalk length (0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5) | |
Sidewalk Damage Level | The ratio of damaged sidewalk length to total sidewalk length (0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5) | |
Sidewalk Cleanliness Level | Ratio of smooth, clean sidewalk length free from illegal parking (0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5) | |
Proportion of Barriers Isolating Sidewalk from Other Lanes | (0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5) | |
Bicycle Lane | Presence of Bicycle Lane | Present = 1/Absent = 0 |
Bicycle Lane Width | Average effective passage width (unit: meters) | |
Bicycle Traffic Volume | Peak-period bicycle flow rate (units: vehicles per minute) | |
Motor Vehicle Lane | Motor Vehicle Lane Width | Average effective passage width (unit: meters) |
Number of Motor Vehicle Lanes | Count (units: number) | |
Motor Vehicle Traffic Condition | Smooth = 1, Slow-moving = 2, Congested = 3, Severe Congestion = 4 | |
Average Vehicle Speed | 0–40 km/h = 1, 40–60 km/h = 2, 60–80 km/h = 3, >80 km/h = 4 | |
Presence of Safety Islands | Present = 1/Absent = 0 | |
Presence of Pedestrian Warning Systems | Present = 1/Absent = 0 | |
Presence of Pedestrian Overpasses | Present = 1/Absent = 0 | |
Presence of Elevated Road Above | Present = 1/Absent = 0 | |
Traffic Lights at Intersections | One intersection with lights = 2/Both intersections = 1/None = 0 | |
Street-Front Buildings | Average Building Height | Average height (unit: meters) |
Number of Motor Vehicle Entrances/Exits | Count (units: number) | |
Proportion of Arcade/Overhanging Space | (0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5) | |
Presence of Objects Intruding into Street | Present = 1/Absent = 0 (e.g., ground-floor encroachment or risk of falling objects from upper floors) | |
Proportion of Commercial Buildings | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Proportion of Residential Buildings | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Proportion of Cultural and Educational Buildings | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Proportion of Sports Facility Buildings | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Proportion of Medical Facility Buildings | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Proportion of Office Buildings | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Landscape and Greenery | Proportion of Street Trees | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 |
Proportion of Child-Interactive Landscapes | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Proportion of Other Landscape Features | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Activity Spaces | Proportion of Usable Activity Space | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 |
Area of Usable Activity Space | 0 m2 = 0, 0–20 m2 = 1, 20–40 m2 = 2, 40–60 m2 = 3, 60–80 m2 = 4, >80 m2 = 5 | |
Service Facilities | Proportion of Bicycle Parking Spaces | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 |
Proportion of Motor Vehicle Parking Spaces | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Lighting System Coverage Rate | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Surveillance System Coverage Rate | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Proportion of Barrier-Free Facilities | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Proportion of Fitness Trails | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Number of Fitness Facilities | Count (units: number) | |
Number of Play Facilities | Count (units: number) | |
Number of Rest Facilities | Count (units: number) | |
Number of Road Signs | Count (units: number) | |
Number of Transit Stops | Count (units: number) | |
Number of Drinking Fountains | Count (units: number) | |
Number of Public Toilets | Count (units: number) | |
Other Elements | Proportion of Non-Motorized Vehicle Parking on Street | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 |
Proportion of Motor Vehicle Parking on Street | 0% = 0, 0–20% = 1, 20–40% = 2, 40–60% = 3, 60–80% = 4, 80–100% = 5 | |
Presence of Odor on Street | Present = 1/Absent = 0 | |
Street Sound Environment | Noisy (>60 dB) = 2/Normal (40–60 dB) = 1/Quiet (<40 dB) = 0 |
Children | Child Caregiver | Expert | Government Department | The Public | |
---|---|---|---|---|---|
Children | 1 | 1 | 2 | 2 | 5 |
Child caregiver | 1 | 1 | 2 | 2 | 5 |
Expert | 1/2 | 1/2 | 1 | 1 | 3 |
Government department | 1/2 | 1/2 | 1 | 1 | 3 |
The public | 1/5 | 1/5 | 1/3 | 1/3 | 1 |
Child-Friendliness Score | 1 Point | 2 Points | 3 Points | 4 Points | 5 Points | SUM |
---|---|---|---|---|---|---|
SUM | 164 | 286 | 562 | 295 | 95 | 1402 |
No. | Universal Child-Friendliness Improvement Measures | SUM |
---|---|---|
01 | Sidewalk Repairs | 233 |
02 | Removal of Obstacles on Sidewalks | 342 |
03 | Regulation of Illegally Parked Non-Motorized and Motorized Vehicles on Sidewalks | 314 |
04 | Addition of Barriers Isolating Sidewalks from Other Traffic Lanes | 362 |
05 | Installation of Speed Reduction Measures | 271 |
06 | Addition of Safe Pedestrian Crossing Facilities | 280 |
07 | Planting of Shade Trees/Installation of Shading Facilities | 413 |
08 | Addition of Interactive Landscapes (e.g., Walkable Lawns, Fountains, Sculptures) | 302 |
09 | Addition of Other Landscape Features (e.g., Shrubs, Flowers) | 300 |
10 | Expansion of Street-Side Areas for Children’s Activities and Rest | 398 |
11 | Addition of Facilities for Children’s Activities and Rest | 835 |
12 | Expansion of Street-Side Areas for Childcare Supervision | 401 |
13 | Addition of Facilities to Facilitate Childcare Supervision | 464 |
14 | Addition of Intergenerational Interaction Facilities for Children | 853 |
15 | Installation of Safety Facilities such as Lighting and Surveillance | 79 |
16 | Addition of Child-Friendly Barrier-Free Facilities | 295 |
17 | Addition of Child-Recognizable Wayfinding Signs | 416 |
18 | Addition of Convenience Facilities such as Children’s Restrooms and Nursing Rooms | 248 |
Child-Friendliness Score | 1 Point | 2 Points | 3 Points | 4 Points | 5 Points | SUM |
---|---|---|---|---|---|---|
SUM | 1312 | 1144 | 1124 | 1180 | 1140 | 5900 |
No. | Universal Child-Friendliness Improvement Measures | SUM |
---|---|---|
01 | Sidewalk Repairs | 1254 |
02 | Removal of Obstacles on Sidewalks | 1485 |
03 | Regulation of Illegally Parked Non-Motorized and Motorized Vehicles on Sidewalks | 1357 |
04 | Addition of Barriers Isolating Sidewalks from Other Traffic Lanes | 1188 |
05 | Installation of Speed Reduction Measures | 1110 |
06 | Addition of Safe Pedestrian Crossing Facilities | 1086 |
07 | Planting of Shade Trees/Installation of Shading Facilities | 1436 |
08 | Addition of Interactive Landscapes (e.g., Walkable Lawns, Fountains, Sculptures) | 1271 |
09 | Addition of Other Landscape Features (e.g., Shrubs, Flowers) | 1311 |
10 | Expansion of Street-Side Areas for Children’s Activities and Rest | 1391 |
11 | Addition of Facilities for Children’s Activities and Rest | 1488 |
12 | Expansion of Street-Side Areas for Childcare Supervision | 1394 |
13 | Addition of Facilities to Facilitate Childcare Supervision | 1421 |
14 | Addition of Intergenerational Interaction Facilities for Children | 1433 |
15 | Installation of Safety Facilities such as Lighting and Surveillance | 1143 |
16 | Addition of Child-Friendly Barrier-Free Facilities | 1411 |
17 | Addition of Child-Recognizable Wayfinding Signs | 1440 |
18 | Addition of Convenience Facilities such as Children’s Restrooms and Nursing Rooms | 1192 |
1 Point | 2 Points | 3 Points | 4 Points | 5 Points | |
---|---|---|---|---|---|
Recall rate | 100% | 99.42% | 86.90% | 100% | 100% |
Accuracy rate | 99.49% | 98.28% | 99.32% | 90.77% | 100% |
Overall accuracy rate | 97.40% |
No. | Universal Child-Friendliness Improvement Measures | Recall Rate | Accuracy Rate | Tag-Level Accuracy Rate |
---|---|---|---|---|
01 | Sidewalk Repairs | 93.41% | 95.50% | 97.28% |
02 | Removal of Obstacles on Sidewalks | 91.85% | 95.38% | |
03 | Regulation of Illegally Parked Non-Motorized and Motorized Vehicles on Sidewalks | 92.63% | 95.65% | |
04 | Addition of Barriers Isolating Sidewalks from Other Traffic Lanes | 98.04% | 96.77% | |
05 | Installation of Speed Reduction Measures | 95.65% | 96.49% | |
06 | Addition of Safe Pedestrian Crossing Facilities | 94.26% | 96.64% | |
07 | Planting of Shade Trees/Installation of Shading Facilities | 95.14% | 94.62% | |
08 | Addition of Interactive Landscapes (e.g., Walkable Lawns, Fountains, Sculptures) | 97.93% | 97.93% | |
09 | Addition of Other Landscape Features (e.g., Shrubs, Flowers) | 97.97% | 96.67% | |
10 | Expansion of Street-Side Areas for Children’s Activities and Rest | 96.37% | 98.97% | |
11 | Addition of Facilities for Children’s Activities and Rest | 99.22% | 98.19% | |
12 | Expansion of Street-Side Areas for Childcare Supervision | 95.36% | 98.93% | |
13 | Addition of Facilities to Facilitate Childcare Supervision | 93.26% | 97.08% | |
14 | Addition of Intergenerational Interaction Facilities for Children | 98.39% | 97.27% | |
15 | Installation of Safety Facilities such as Lighting and Surveillance | 95.83% | 95.83% | |
16 | Addition of Child-Friendly Barrier-Free Facilities | 99.27% | 100% | |
17 | Addition of Child-Recognizable Wayfinding Signs | 93.98% | 95.31% | |
18 | Addition of Convenience Facilities such as Children’s Restrooms and Nursing Rooms | 99.04% | 100% |
Road Number | Child-Friendliness Score | Road Number | Child-Friendliness Score | Road Number | Child-Friendliness Score | Road Number | Child-Friendliness Score |
---|---|---|---|---|---|---|---|
011a | 3 | 062b | 4 | 104a | 2 | 151b | 5 |
011b | 5 | 063a | 4 | 104b | 2 | 152a | 4 |
012a | 3 | 063b | 3 | 111a | 3 | 152b | 4 |
012b | 1 | 071a | 3 | 111b | 1 | 161a | 2 |
013a | 3 | 071b | 3 | 112a | 3 | 161b | 3 |
013b | 5 | 072a | 3 | 112b | 4 | 162a | 3 |
014a | 4 | 072b | 4 | 113a | 3 | 162b | 3 |
014b | 3 | 073a | 4 | 113b | 1 | 163a | 1 |
015a | 3 | 073b | 3 | 121a | 3 | 163b | 2 |
015b | 3 | 081a | 3 | 121b | 2 | 171a | 3 |
021a | 1 | 081b | 3 | 122a | 4 | 171b | 2 |
021b | 1 | 082a | 2 | 122b | 3 | 172a | 3 |
031a | 3 | 082b | 3 | 123a | 4 | 172b | 4 |
031b | 1 | 083a | 4 | 123b | 3 | 173a | 2 |
032a | 4 | 083b | 3 | 131a | 5 | 173b | 1 |
032b | 3 | 084a | 3 | 131b | 3 | 174a | 3 |
041a | 3 | 084b | 1 | 132a | 3 | 174b | 3 |
041b | 4 | 091a | 2 | 132b | 2 | 181a | 2 |
051a | 3 | 091b | 2 | 141a | 5 | 181b | 3 |
051b | 1 | 101a | 4 | 141b | 3 | 182a | 5 |
052a | 1 | 101b | 4 | 142a | 2 | 182b | 3 |
052b | 2 | 102a | 1 | 142b | 4 | 183a | 4 |
061a | 2 | 102b | 1 | 143a | 2 | 183b | 2 |
061b | 2 | 103a | 2 | 143b | 5 | ||
062a | 2 | 103b | 1 | 151a | 3 |
01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
score1 | 012b | √ | √ | √ | √ | ||||||||||||||
021a | √ | √ | √ | √ | |||||||||||||||
021b | √ | √ | √ | √ | √ | ||||||||||||||
031b | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | |||||||||
051b | √ | √ | √ | √ | √ | ||||||||||||||
052a | √ | √ | √ | √ | |||||||||||||||
084b | √ | √ | √ | √ | √ | √ | |||||||||||||
102a | √ | √ | √ | √ | √ | √ | √ | √ | |||||||||||
102b | √ | √ | √ | √ | √ | √ | √ | √ | √ | ||||||||||
103b | √ | √ | √ | √ | √ | √ | |||||||||||||
111b | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | |||||||||
113b | √ | √ | √ | √ | √ | ||||||||||||||
163a | √ | √ | √ | √ | √ | √ | √ | ||||||||||||
173b | √ | √ | √ | √ | √ | √ | |||||||||||||
score2 | 052b | √ | √ | √ | |||||||||||||||
061a | √ | √ | √ | ||||||||||||||||
061b | √ | √ | √ | ||||||||||||||||
062a | √ | √ | √ | √ | √ | ||||||||||||||
082a | √ | √ | √ | ||||||||||||||||
091a | √ | √ | √ | √ | |||||||||||||||
091b | √ | √ | √ | √ | √ | ||||||||||||||
103a | √ | √ | √ | √ | √ | ||||||||||||||
104a | √ | √ | √ | ||||||||||||||||
104b | √ | √ | √ | √ | |||||||||||||||
121b | √ | √ | √ | √ | √ | ||||||||||||||
132b | √ | √ | √ | ||||||||||||||||
142a | √ | √ | √ | √ | √ | √ | √ | √ | √ | ||||||||||
143a | √ | √ | √ | √ | √ | √ | √ | ||||||||||||
161a | √ | √ | √ | √ | √ | √ | √ | √ | |||||||||||
163b | √ | √ | √ | ||||||||||||||||
171b | √ | √ | |||||||||||||||||
173a | √ | √ | √ | √ | √ | ||||||||||||||
181a | √ | √ | √ | ||||||||||||||||
183b | √ | √ | √ | √ | √ | √ | √ | √ | √ |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tu, H.; Miao, X.; Jin, S.; Yang, J.; Miao, X.; Qi, J. Deep Learning-Based Systems for Evaluating and Enhancing Child-Friendliness of Urban Streets—A Case of Shanghai Urban Street. Buildings 2025, 15, 2291. https://doi.org/10.3390/buildings15132291
Tu H, Miao X, Jin S, Yang J, Miao X, Qi J. Deep Learning-Based Systems for Evaluating and Enhancing Child-Friendliness of Urban Streets—A Case of Shanghai Urban Street. Buildings. 2025; 15(13):2291. https://doi.org/10.3390/buildings15132291
Chicago/Turabian StyleTu, Huijun, Xudong Miao, Shitao Jin, Jiayi Yang, Xinyue Miao, and Jiale Qi. 2025. "Deep Learning-Based Systems for Evaluating and Enhancing Child-Friendliness of Urban Streets—A Case of Shanghai Urban Street" Buildings 15, no. 13: 2291. https://doi.org/10.3390/buildings15132291
APA StyleTu, H., Miao, X., Jin, S., Yang, J., Miao, X., & Qi, J. (2025). Deep Learning-Based Systems for Evaluating and Enhancing Child-Friendliness of Urban Streets—A Case of Shanghai Urban Street. Buildings, 15(13), 2291. https://doi.org/10.3390/buildings15132291