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題名 智慧建築附設停車空間使用率及停車需求模型之研究
The Study of Parking Space Demand Model of Smart Building in Taipei City
作者 陳冠宇
Chen, Kuan-Yu
貢獻者 白仁德<br>孫振義
Pai, Jen-Te<br>Sun, Chen-Yi
陳冠宇
Chen, Kuan-Yu
關鍵詞 智慧建築
建築物附設停車空間
機器學習
停車供需分析
停車需求模型
Machine Learning
Smart Building
Parking Supply and Demand Analysis
Parking Demand Model
日期 2024
上傳時間 4-Sep-2024 14:27:53 (UTC+8)
摘要 隨著汽車的普及，停車問題成為各發展中國家所面臨的挑戰，完善的停車規範將有助於合理地分配停車資源，預防交通擁擠與違規情形發生，而建築物附設停車空間作為停車位供給之大宗，其使用情形對於整體停車供給至關重要，若能掌握準確的建築物停車使用率資料，透過統計分析及時顯示建築物閒置停車位資料，對都市停車環境之改善有正面效用，基於現正蓬勃發展的智慧建築擁有大量、多樣、即時更新之感測資料可供加值應用，並具備優良之物聯網環境，其中智慧建築營運所產生之停車使用率資料量大且具有串接跨域數據之價值。 故為提高智慧建築資料再利用之經濟與社會價值，本研究以臺北市智慧建築作為研究對象，蒐集30處臺北市之智慧建築，涵蓋各行政區與各式建物使用用途，分析智慧建築停車資料，探討現有智慧建築物附設停車空間之供需概況與法規規範；再透過文獻回顧，挑選影響停車需求之變數，建構模型以預測停車需求，並以人工智慧之機器學習方法，使用監督式學習，利用多種迴歸模型對停車需求進行預測或分類，後續使用主成分分析與交叉驗證等手段，建構K-Nearest Neighbors機器學習模型，其預測準確度良好並有效預測各智慧建物停車服務水準。模型結果能針對各新建智慧建築之自設停車位數給予建議，以及現有各種不同用途使用之智慧建築給予停車管理建議。
With the widespread use of automobiles, parking has become a significant challenge for developing countries. Well-defined parking regulations aid in the equitable distribution of parking resources, preventing traffic congestion and violations. Parking spaces within buildings constitute a major supply, and their efficient utilization is crucial for overall parking management. Accurate data on building parking usage, analyzed through statistical methods, can effectively highlight vacant parking spaces, positively impacting urban parking environments. Smart buildings, equipped with diverse and real-time sensor data in a robust IoT environment, generate substantial parking usage data valuable for interdisciplinary integration. This study focuses on Taipei's smart buildings, analyzing parking data from 30 locations across various districts and building types. It investigates the current supply-demand scenario and regulatory compliance of parking spaces. Through literature review, key variables influencing parking demand are identified to construct predictive models using supervised machine learning techniques, including multiple regression, logistic regression and K-Nearest Neighbors. These models accurately forecast parking demand and service levels, providing recommendations for new and existing smart buildings to optimize parking space allocation and management.
參考文獻 中文參考文獻 1. 賀士麃（2013）。鼓勵建築物增設停車空間使用現況調查之研究-以臺北市大安區、中山區為例。桃園創新學報，（33），167-177。 2. 張怡文（2017）。智慧建築資料開放應用調查之研究。 3. 內政部建築研究所（2024）。智慧建築評估手冊。 4. 內政部（2019）。內政部統計通報。 5. 中華民國統計資訊網（2023）。https://www.stat.gov.tw/。 6. 鍾凱如（2022）。臺北市公共自行車使用特性與空間分佈型態之趨勢變化分析。 7. 總統盃黑客松（2024）。https://presidential-hackathon.taiwan.gov.tw/。 8. 交通部（2010）。交通工程手冊。 9. 臺北市政府（2022）。111年臺北市汽機車供需調查。 10. 內政部國土管理署（2024）。內政部主管法規建築技術規則建築設計施工編。 11. 臺北市政府都市發展局（2024）。臺北市土地使用分區管制自治條例。 12. 交通部（2024）。停車場法。 13. 邱瓊玉，2005，以永續觀點檢討建築停車空間設置標準之研究，內政部建築研究所自行研究報告。 14. 陳致堯，2017，臺北市社會住宅區位選址指標分析，社區發展季刊，33-47。 15. 朱建全、林亨杰、葉斯文、王維瑩（2013）。新北市不同土地使用旅次發生與停車需求調查研究。都市交通，27，184-203。 16. 吳玫芳、周世璋、黃俊霖（2013）。臺北市建築物附設機車停車空間使用管理之研究，物業管理學報，4（2），15-24。 17. 陳健忠，黃光宇，蔡佩容（2019），人工智慧方法應用於停車場資訊系統之建置，嶺東學報，（45），133-153。 18. 陳嘉懿（2012）。全球智慧綠建築案例分享，https://reurl.cc/3x4pEL。 19. 桃園市政府交通局（2019）。106年度桃園市公有停車智慧化管理策略研究計畫結果。 20. 中鼎集團（2024）。屏東勝利公園—智慧建築地下停車場 21. 研華科技（2024）。研華林口物聯網智慧園區 22. 內政部建築研究所（2020），智慧建築雲端平臺應用推廣計畫。 23. 中華電信集團（2016），光世代雲端智慧綠建築系統。 24. 香港運輸署（2024）。香港交通政策白皮書 外文參考文獻 1. Hogg, R. V., & Tanis, E. A. (2015). Probability and statistical inference (9th ed.). Pearson. 2. Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (5th ed.). Wiley. 3. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). Wiley. 4. Cover, T. M., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. 5. Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification (2nd ed.). Wiley. 6. Gay, L. R. (1992). Educational research: Competencies for analysis and application (4th ed.). Macmillan Publishing Company. 7. Favaretto, M., De Clercq, E., Schneble, C. O., & Elger, B. S. (2020). What is your definition of Big Data? Researchers’ understanding of the phenomenon of the decade. PloS one, 15(2), e0228987. 8. Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71(2), 579-625. 9. Elgendy, N., & Elragal, A. (2014). Big data analytics: a literature review paper. In Advances in Data Mining. Applications and Theoretical Aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings 14 (pp. 214-227). Springer International Publishing. 10. Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of parallel and distributed computing, 74(7), 2561-2573. 11. Zantalis, F., Koulouras, G., Karabetsos, S., & Kandris, D. (2019). A review of machine learning and IoT in smart transportation. Future Internet, 11(4), 94. 12. Aliero, M. S., Asif, M., Ghani, I., Pasha, M. F., & Jeong, S. R. (2022). Systematic Review Analysis on Intelligent building: Challenges and Opportunities. Sustainability, 14(5), 3009. 13. Plageras, A. P., Psannis, K. E., Stergiou, C., Wang, H., & Gupta, B. B. (2018). Efficient IoT-based sensor BIG Data collection–processing and analysis in intelligent buildings. Future Generation Computer Systems, 82, 349-357. 14. Shoup, D. (2005). The High Cost of Free Parking. Planners Press. 15. Litman, T. (2020). Autonomous vehicle implementation predictions: Implications for transport planning. 16. Hess, D. J. (2001). Ethnography and the development of science and technology studies (pp. 234-245). na. 17. Marsden, G. (2006). The evidence base for parking policies—a review. Transport policy, 13(6), 447-457. 18. Zanni, A. M., & Ryley, T. J. (2015). The impact of extreme weather conditions on long distance travel behaviour. Transportation Research Part A: Policy and Practice, 77, 305-319. 19. Guo, Z., & Ferreira Jr, J. (2008). Pedestrian environments, transit path choice, and transfer penalties: Understanding land-use impacts on transit travel. Environment and Planning B: Planning and Design, 35(3), 461-479. 20. Khattak, A. J., & De Palma, A. (1997). The impact of adverse weather conditions on the propensity to change travel decisions: a survey of Brussels commuters. Transportation Research Part A: Policy and Practice, 31(3), 181-203. 21. Manville, M., & Shoup, D. C. (2010). Parking requirements as a barrier to housing development: regulation and reform in Los Angeles. 22. Giuliano, G. (1989). Incident characteristics, frequency, and duration on a high volume urban freeway. Transportation Research Part A: General, 23(5), 387-396. 23. Théry, C., Witwer, K. W., Aikawa, E., Alcaraz, M. J., Anderson, J. D., Andriantsitohaina, R., ... & Jovanovic‐Talisman, T. (2018). Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. Journal of extracellular vesicles, 7(1), 1535750. 24. Meacham-Hensold, K., Montes, C. M., Wu, J., Guan, K., Fu, P., Ainsworth, E. A., ... & Bernacchi, C. J. (2019). High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity. Remote sensing of environment, 231, 111176. 25. Campos-Taberner, M., García-Haro, F. J., Martínez, B., Izquierdo-Verdiguier, E., Atzberger, C., Camps-Valls, G., & Gilabert, M. A. (2020). Understanding deep learning in land use classification based on Sentinel-2 time series. Scientific reports, 10(1), 17188. 26. Willson, R., O'connor, T., & Hajjiri, S. (2012). Parking at affordable housing: Study results in San Diego, California. Transportation research record, 2319(1), 13-20. 27. Jae-Myung, H. (2004). A Study on the Parking Demand of Apartment Complex in Daegu city. Journal of the Korean housing association, 15(5), 51-58. 28. Alyssa B. Sherman, 2010, “The Effects of Residential Off-Street Parking Availability on Travel Behavior in San Francisco”, Urban and Regional Planning San Jose State University, the Degree Master of Urban Planning. 29. Pendola, R., Ruddy, S., & Tosta, E. (2005). Residential Parking Requirements in San Francisco: Do They Affect Travel Behavior?. Unpublished report presented to Livable City by San Francisco State University Urban Studies Program. 30. Adler, T., & Ben-Akiva, M. (1979). A theoretical and empirical model of trip chaining behavior. Transportation Research Part B: Methodological, 13(3), 243-257. 31. Buckman, A. H., Mayfield, M., & Beck, S. B. (2014). What is a intelligent building?. Smart and Sustainable Built Environment, 3(2), 92-109. 32. Chester, M., Fraser, A., Matute, J., Flower, C., & Pendyala, R. (2015). Parking infrastructure: A constraint on or opportunity for urban redevelopment? A study of Los Angeles County parking supply and growth. Journal of the American Planning Association, 81(4), 268-286. 33. Gabbe, C. J. (2018). How do developers respond to land use regulations? An analysis of new housing in Los Angeles. Housing Policy Debate, 28(3), 411-427. 34. Gabbe, C. J., Pierce, G., & Clowers, G. (2020). Parking policy: The effects of residential minimum parking requirements in Seattle. Land Use Policy, 91, 104053. 35. Hoehne, C. G., Chester, M. V., Fraser, A. M., & King, D. A. (2019). Valley of the sun-drenched parking space: The growth, extent, and implications of parking infrastructure in Phoenix. Cities, 89, 186-198. 36. Inci, E. (2015). A review of the economics of parking. Economics of Transportation, 4(1-2), 50-63. 37. Jia, M., Komeily, A., Wang, Y., & Srinivasan, R. S. (2019). Adopting Internet of Things for the development of intelligent buildings: A review of enabling technologies and applications. Automation in Construction, 101, 111-126. 38. Minoli, D., Sohraby, K., & Occhiogrosso, B. (2017). IoT considerations, requirements, and architectures for intelligent buildings—Energy optimization and next-generation building management systems. IEEE Internet of Things Journal, 4(1), 269-283. 39. Parmar, J., Das, P., & Dave, S. M. (2020). Study on demand and characteristics of parking system in urban areas: A review. Journal of Traffic and Transportation Engineering (English Edition), 7(1), 111-124. 40. Verma, A., Prakash, S., Srivastava, V., Kumar, A., & Mukhopadhyay, S. C. (2019). Sensing, controlling, and IoT infrastructure in intelligent building: a review. IEEE Sensors Journal, 19(20), 9036-9046. 41. Zantalis, F., Koulouras, G., Karabetsos, S., & Kandris, D. (2019). A review of machine learning and IoT in smart transportation. Future Internet, 11(4), 94.
描述 碩士
國立政治大學
地政學系
111257018
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111257018
資料類型 thesis
dc.contributor.advisor 白仁德<br>孫振義zh_TW
dc.contributor.advisor Pai, Jen-Te<br>Sun, Chen-Yien_US
dc.contributor.author (Authors) 陳冠宇zh_TW
dc.contributor.author (Authors) Chen, Kuan-Yuen_US
dc.creator (作者) 陳冠宇zh_TW
dc.creator (作者) Chen, Kuan-Yuen_US
dc.date (日期) 2024en_US
dc.date.accessioned 4-Sep-2024 14:27:53 (UTC+8)-
dc.date.available 4-Sep-2024 14:27:53 (UTC+8)-
dc.date.issued (上傳時間) 4-Sep-2024 14:27:53 (UTC+8)-
dc.identifier (Other Identifiers) G0111257018en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/153251-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 地政學系zh_TW
dc.description (描述) 111257018zh_TW
dc.description.abstract (摘要) 隨著汽車的普及，停車問題成為各發展中國家所面臨的挑戰，完善的停車規範將有助於合理地分配停車資源，預防交通擁擠與違規情形發生，而建築物附設停車空間作為停車位供給之大宗，其使用情形對於整體停車供給至關重要，若能掌握準確的建築物停車使用率資料，透過統計分析及時顯示建築物閒置停車位資料，對都市停車環境之改善有正面效用，基於現正蓬勃發展的智慧建築擁有大量、多樣、即時更新之感測資料可供加值應用，並具備優良之物聯網環境，其中智慧建築營運所產生之停車使用率資料量大且具有串接跨域數據之價值。 故為提高智慧建築資料再利用之經濟與社會價值，本研究以臺北市智慧建築作為研究對象，蒐集30處臺北市之智慧建築，涵蓋各行政區與各式建物使用用途，分析智慧建築停車資料，探討現有智慧建築物附設停車空間之供需概況與法規規範；再透過文獻回顧，挑選影響停車需求之變數，建構模型以預測停車需求，並以人工智慧之機器學習方法，使用監督式學習，利用多種迴歸模型對停車需求進行預測或分類，後續使用主成分分析與交叉驗證等手段，建構K-Nearest Neighbors機器學習模型，其預測準確度良好並有效預測各智慧建物停車服務水準。模型結果能針對各新建智慧建築之自設停車位數給予建議，以及現有各種不同用途使用之智慧建築給予停車管理建議。zh_TW
dc.description.abstract (摘要) With the widespread use of automobiles, parking has become a significant challenge for developing countries. Well-defined parking regulations aid in the equitable distribution of parking resources, preventing traffic congestion and violations. Parking spaces within buildings constitute a major supply, and their efficient utilization is crucial for overall parking management. Accurate data on building parking usage, analyzed through statistical methods, can effectively highlight vacant parking spaces, positively impacting urban parking environments. Smart buildings, equipped with diverse and real-time sensor data in a robust IoT environment, generate substantial parking usage data valuable for interdisciplinary integration. This study focuses on Taipei's smart buildings, analyzing parking data from 30 locations across various districts and building types. It investigates the current supply-demand scenario and regulatory compliance of parking spaces. Through literature review, key variables influencing parking demand are identified to construct predictive models using supervised machine learning techniques, including multiple regression, logistic regression and K-Nearest Neighbors. These models accurately forecast parking demand and service levels, providing recommendations for new and existing smart buildings to optimize parking space allocation and management.en_US
dc.description.tableofcontents 目次 I 表次 II 圖次 III 第一章 緒論 1 第一節 研究動機與目的 1 第二節 研究範疇 5 第三節 研究方法 7 第四節 研究內容與流程 9 第二章 文獻回顧 13 第一節 智慧建築之界定 13 第二節 大數據分析結合智慧建築資料之應用 17 第三節 智慧停車管理相關案例 25 第四節 停車供需分析相關文獻 30 第五節 建築物附設停車相關法規政策 40 第三章 研究設計 43 第一節 研究架構 43 第二節 研究方法與分析步驟 44 第三節 統計分析與模型設計 46 第四章 實證分析 55 第一節 統計分析 55 第二節 模型實證分析 68 第三節 小結 79 第五章 結論與建議 83 第一節 結論 83 第二節 建議 85 參考書目 87zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111257018en_US
dc.subject (關鍵詞) 智慧建築zh_TW
dc.subject (關鍵詞) 建築物附設停車空間zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 停車供需分析zh_TW
dc.subject (關鍵詞) 停車需求模型zh_TW
dc.subject (關鍵詞) Machine Learningen_US
dc.subject (關鍵詞) Smart Buildingen_US
dc.subject (關鍵詞) Parking Supply and Demand Analysisen_US
dc.subject (關鍵詞) Parking Demand Modelen_US
dc.title (題名) 智慧建築附設停車空間使用率及停車需求模型之研究zh_TW
dc.title (題名) The Study of Parking Space Demand Model of Smart Building in Taipei Cityen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 中文參考文獻 1. 賀士麃（2013）。鼓勵建築物增設停車空間使用現況調查之研究-以臺北市大安區、中山區為例。桃園創新學報，（33），167-177。 2. 張怡文（2017）。智慧建築資料開放應用調查之研究。 3. 內政部建築研究所（2024）。智慧建築評估手冊。 4. 內政部（2019）。內政部統計通報。 5. 中華民國統計資訊網（2023）。https://www.stat.gov.tw/。 6. 鍾凱如（2022）。臺北市公共自行車使用特性與空間分佈型態之趨勢變化分析。 7. 總統盃黑客松（2024）。https://presidential-hackathon.taiwan.gov.tw/。 8. 交通部（2010）。交通工程手冊。 9. 臺北市政府（2022）。111年臺北市汽機車供需調查。 10. 內政部國土管理署（2024）。內政部主管法規建築技術規則建築設計施工編。 11. 臺北市政府都市發展局（2024）。臺北市土地使用分區管制自治條例。 12. 交通部（2024）。停車場法。 13. 邱瓊玉，2005，以永續觀點檢討建築停車空間設置標準之研究，內政部建築研究所自行研究報告。 14. 陳致堯，2017，臺北市社會住宅區位選址指標分析，社區發展季刊，33-47。 15. 朱建全、林亨杰、葉斯文、王維瑩（2013）。新北市不同土地使用旅次發生與停車需求調查研究。都市交通，27，184-203。 16. 吳玫芳、周世璋、黃俊霖（2013）。臺北市建築物附設機車停車空間使用管理之研究，物業管理學報，4（2），15-24。 17. 陳健忠，黃光宇，蔡佩容（2019），人工智慧方法應用於停車場資訊系統之建置，嶺東學報，（45），133-153。 18. 陳嘉懿（2012）。全球智慧綠建築案例分享，https://reurl.cc/3x4pEL。 19. 桃園市政府交通局（2019）。106年度桃園市公有停車智慧化管理策略研究計畫結果。 20. 中鼎集團（2024）。屏東勝利公園—智慧建築地下停車場 21. 研華科技（2024）。研華林口物聯網智慧園區 22. 內政部建築研究所（2020），智慧建築雲端平臺應用推廣計畫。 23. 中華電信集團（2016），光世代雲端智慧綠建築系統。 24. 香港運輸署（2024）。香港交通政策白皮書 外文參考文獻 1. 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