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| Title: | 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 |
| Authors: | 陳冠宇
Chen, Kuan-Yu |
| Contributors: | 白仁德
孫振義
Pai, Jen-Te
Sun, Chen-Yi
陳冠宇
Chen, Kuan-Yu |
| Keywords: | 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 |
| Date: | 2024 |
| Issue Date: | 2024-09-04 14:27:53 (UTC+8) |
| Abstract: | 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. |
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| Description: | 碩士
國立政治大學
地政學系
111257018 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111257018 |
| Data Type: | thesis |
| Appears in Collections: | [地政學系] 學位論文
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