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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 詹瀅潔 | zh_TW |
| dc.contributor.advisor | Ying-Chieh Chan | en |
| dc.contributor.author | 郭銘晉 | zh_TW |
| dc.contributor.author | Ming-Ching Kuo | en |
| dc.date.accessioned | 2025-07-30T16:17:41Z | - |
| dc.date.available | 2025-07-31 | - |
| dc.date.copyright | 2025-07-30 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-17 | - |
| dc.identifier.citation | [1] 王柏喻（2023）。以運動攝影機輔助台北市人行道環境評估。﹝碩士論文。國立臺灣大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/g5qdhs
[2] 顏騰涁（2023）。評估防窗殺貼紙圖案設計對窗景的影響。﹝碩士論文。國立臺灣大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/cv9hr3 [3] Argota Sánchez-Vaquerizo, J., Hausladen, C., Mahajan, S., Matter, M., Siebenmann, M., van Eggermond, M., & Helbing, D. (2024). A virtual reality experiment to study pedestrian perception of future street scenarios. Scientific Reports, 14. https://doi.org/10.1038/s41598-024-55073-x [4] Asgarzadeh, M., Koga, T., Hirate, K., Farvid, M., & Lusk, A. (2014). Investigating oppressiveness and spaciousness in relation to building, trees, sky and ground surface: A study in Tokyo. Landscape and Urban Planning, 131, 36-41. https://doi.org/https://doi.org/10.1016/j.landurbplan.2014.07.011 [5] Barnett, A., Van Dyck, D., Van Cauwenberg, J., Zhang, C. J. P., Lai, P. C., & Cerin, E. (2020). Objective neighbourhood attributes as correlates of neighbourhood dissatisfaction and the mediating role of neighbourhood perceptions in older adults from culturally and physically diverse urban environments. 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Places for play: Understanding human perception of playability in cities using street view images and deep learning. Computers, Environment and Urban Systems, 90, 101693. https://doi.org/https://doi.org/10.1016/j.compenvurbsys.2021.101693 [18] Li, X., Ratti, C., & Seiferling, I. (2018). Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View. Landscape and Urban Planning, 169, 81-91. https://doi.org/https://doi.org/10.1016/j.landurbplan.2017.08.011 [19] Li, Y., Yabuki, N., & Fukuda, T. (2022). Measuring visual walkability perception using panoramic street view images, virtual reality, and deep learning. Sustainable Cities and Society, 86, 104140. https://doi.org/https://doi.org/10.1016/j.scs.2022.104140 [20] Liu, C., Yu, Y., & Yang, X. (2024). 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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98195 | - |
| dc.description.abstract | 本研究聚焦於台北市住宅區周邊街道環境的參數化分析，並探討其與居民感知之間的相互關係，目的在於補足現行都市規劃中對住戶主觀視覺偏好的不足。本研究選取台北市31個地點，結合地理資訊系統 (GIS) 和公共數據，取得包含道路寬度、建物樓層、人行道寬度在內的共計16項都市環境參數，並透過360度全景相機錄製環境影像。作為參數的一部份，本研究也透過DeepLabV3預訓練模型進行影像辨識後，用以計算視域因子。為深入探討這些環境參數與居民感知的關聯性，本研究運用虛擬實境 (VR) 技術進行實驗，邀請共計62位實驗參與者透過李克特量表評估七種感官面向，包括安全性、有趣程度、美觀性、寬敞感、環境活力、居住意願及鄰里生活意願。
研究結果顯示，不同的感官評價指標對應不同街道環境參數。包含，道路寬度和人行道寬度對安全性評價影響顯著，而行人與車輛流量則影響趣味性評估。美觀性方面，GVF是主要指標，顯示綠化程度對城市景觀的重要性。環境活力與行人流量密切相關，而建築高度則影響居住及鄰里生活意願。 研究結果除了突顯街道環境參數對都市規劃的價值，更揭示了各種都市環境參數對不同感受評估的影響，為未來的都市規劃提供新視角與基礎。持續的研究與發展將使都市規劃更加重視居住者的感官偏好，具有相當正面的發展潛力，並促進更宜居的城市空間及生活環境。 | zh_TW |
| dc.description.abstract | This study focuses on the parametric analysis of street environments surrounding residential areas in Taipei City and explores their relationship with residents' perceptions, aiming to address the lack of consideration for subjective visual preferences in current urban planning. A total of 31 locations in Taipei City were selected, and 16 urban environmental parameters, including road width, building height, and sidewalk width, were obtained by integrating Geographic Information Systems (GIS) and public data. Additionally, 360-degree panoramic images were recorded using a panoramic camera. As part of the parameters, this study also utilized the DeepLabV3 pre-trained model for image recognition to calculate the View Factor. To further explore the relationship between these environmental parameters and residents' perceptions, virtual reality (VR) technology was employed in experiments. A total of 62 participants were invited to evaluate seven sensory aspects—safety, interesting, aesthetics, spaciousness, liveliness, willingness to live, and neighborhood living willingness—using a Likert scale.
The results reveal that different sensory evaluation indicators correspond to different street environmental parameters. For instance, road width and sidewalk width significantly influence safety evaluations, while pedestrian and vehicular flow impact interesting evaluations. In terms of aesthetics, the Green View Factor (GVF) emerged as a key indicator, highlighting the importance of greenery in urban landscapes. Environmental liveliness was closely related to pedestrian flow, whereas building height influenced both willingness to live and neighborhood living willingness. In addition to emphasizing the value of street environmental parameters for urban planning, the findings reveal the impact of various urban parameters on different sensory evaluations, offering new perspectives and foundations for future urban planning. Continued research and development in this field are expected to enhance the emphasis on residents' sensory preferences in urban planning, fostering more livable urban spaces and environments with significant potential for positive development. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-30T16:17:41Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-30T16:17:41Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENT v LIST OF FIGURES ix LIST OF TABLES xiii Chapter 1 Introduction 1 Chapter 2 Literature Review 3 2.1 Streetscape Evaluation Methods and Criteria 3 2.2 Environmental Parameters 6 2.2.1 Urban scale geometric parameters 6 2.2.2 View Factor and View Index. 6 2.3 Street View Image (SVI) and Panoramic images 7 2.4 Application of Semantic Segmentation 8 2.5 Research Gap and Summary 9 Chapter 3 Methodology 11 3.1 Data Collection 13 3.1.1 Panoramic Video shooting 13 3.1.2 GIS public data collection 15 3.2 Parameters Calculation 16 3.2.1 Parameters captured from Panoramic video 18 3.2.2 Parameters captured from Public Data and Geographic Information System 18 3.2.3 Parameters Derived from Semantic Segmentation 20 3.3 Virtual Reality Experiment Execution 28 3.3.1 Experimental Device 28 3.3.2 Video Processing 29 3.3.3 Questionnaire design 29 3.3.4 Recruitment of participants and research authorization 30 3.3.5 Implementation of Virtual Reality Experiment 30 3.4 Statistical Validation and Power Analysis 32 Chapter 4 Result and Discussion 34 4.1 Kruskal-Wallis H test data grouping 34 4.2 Results and Discussion on Safety Analysis 36 4.2.1 Analysis of Road Width and Safety Evaluation Results 37 4.2.2 Analysis of Sidewalk Width and Safety Evaluation Results 40 4.2.3 Discussion of Other Significant Parameters Impact on Safety Evaluation 43 4.2.4 Random Forest and SHAP-Based Analysis of Safety Perception 46 4.2.5 Post-experiment interviews and word cloud analysis on safety. 47 4.3 Results and Discussion on Interest Analysis 50 4.3.1 Analysis of People Flow and Interesting Evaluation Results 51 4.3.2 Analysis of Vehicle and Interesting Evaluation Results 53 4.3.3 Discussion of Other Significant Parameters Impact on Interesting Evaluation 55 4.3.4 Random Forest and SHAP-Based Analysis of Interesting Perception 57 4.3.5 Post-experiment interviews and Word Cloud analysis on Interesting. 59 4.4 Results and Discussion on Aesthetic Analysis 61 4.4.1 Analysis of BVF and Aesthetic Evaluation Results 62 4.4.2 Analysis of Building Height and Aesthetic Evaluation Results 65 4.4.3 Analysis of GVF and Aesthetic Evaluation Results 67 4.4.4 Discussion of Other Significant Parameters Impact on Aesthetic Evaluation 69 4.4.5 Random Forest and SHAP-Based Analysis of Aesthetic Perception 71 4.4.6 Post-experiment interviews and Word Cloud analysis on aesthetic 73 4.5 Results and Discussion on Spaciousness Analysis 75 4.5.1 Analysis of Road Width and Spaciousness Evaluation Results 76 4.5.2 Analysis of Sidewalk Width and Spaciousness Evaluation Results 79 4.5.3 Discussion of Other Significant Parameters Impact on Spaciousness Evaluation 81 4.5.4 Random Forest and SHAP-Based Analysis of Spaciousness Perception 83 4.5.5 Post-experiment interviews and Word Coud on spaciousness 85 4.6 Results and Discussion on Liveliness Analysis 87 4.6.1 Analysis of People Flow and Liveliness Evaluation Results 88 4.6.2 Discussion of Other Significant Parameters Impact on Liveliness Evaluation 90 4.6.3 Random Forest and SHAP-Based Analysis of Liveliness Perception 92 4.6.4 Post-experiment interviews and Word Cloud on Liveliness 93 4.7 Results and Discussion on Live Willingness 96 4.7.1 Analysis of Building Height and Live Willingness 97 4.7.2 Analysis of Sidewalk Ratio and Residential Live Willingness Results 99 4.7.3 Discussion of Other Significant Parameters Impact on Live Willingness 101 4.7.4 Random Forest and SHAP-Based Analysis of Live Willingness Perception 102 4.7.5 Post-experiment Interviews and Word Cloud on Live Willingness Results 104 4.8 Results and Discussion on Neighborhood Willingness 106 4.8.1 Analysis of Building Height and Neighborhood Willingness Evaluation Results 107 4.8.2 Discussion of Other Significant Parameters Impact on Neighborhood Willingness Evaluation 109 4.8.3 Random Forest and SHAP-Based Analysis of Neighborhood Willingness Perception 111 4.8.4 Post-experiment Interviews and Word Cloud on Neighborhood Willingness Evaluation Results 112 4.9 Discussion in Relation to Prior Research 115 Chapter 5 Research Limitations and Future Work 117 5.1 Research Limitations 117 5.2 Future Work 119 Chapter 6 Conclusion 121 REFERENCE 124 APPENDIX -Streetscape Parameters by Location 129 | - |
| dc.language.iso | en | - |
| dc.subject | 地理資訊系統 | zh_TW |
| dc.subject | 街道環境 | zh_TW |
| dc.subject | 感官實驗 | zh_TW |
| dc.subject | 虛擬實境 | zh_TW |
| dc.subject | 全景影像 | zh_TW |
| dc.subject | Panorama Imagery | en |
| dc.subject | Virtual Reality (VR) | en |
| dc.subject | Sensory Experiment | en |
| dc.subject | Geographic Information Systems (GIS) | en |
| dc.subject | Street Environment | en |
| dc.title | 基於全景影像與虛擬實境的城市住宅街景感知評估 | zh_TW |
| dc.title | Evaluation of Urban Residential Streetscape Perception Based on Panoramic Imagery and Virtual Reality | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 許聿廷;王琨淇 | zh_TW |
| dc.contributor.oralexamcommittee | Yu-Ting Hsu;Kun-Chi Wang | en |
| dc.subject.keyword | 街道環境,地理資訊系統,全景影像,虛擬實境,感官實驗, | zh_TW |
| dc.subject.keyword | Street Environment,Geographic Information Systems (GIS),Panorama Imagery,Virtual Reality (VR),Sensory Experiment, | en |
| dc.relation.page | 144 | - |
| dc.identifier.doi | 10.6342/NTU202501829 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-07-18 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2025-07-31 | - |
| 顯示於系所單位： | 土木工程學系 | |
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