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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93347


    Title: 區域停車場最佳月租金之研究–以混和型住宅區為例;A study on optimal monthly parking fees for regional parking lots at mixed residential areas
    Authors: 賴秉勳;Lai, Ping-Hsun
    Contributors: 土木系營建管理碩士在職專班
    Keywords: 每月停車費;多元迴歸;情境分析;混合住宅區
    Date: 2024-01-26
    Issue Date: 2024-03-05 16:24:57 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 住宅物業經常面臨停車位不足的挑戰,導致需要租賃外部停車設施來滿足居民的停車需求。消費者特別關注相關的租賃成本,凸顯了合理確定每月停車費的必要性。本研究旨在製定和優化適合台灣城市地區混合住宅區的每月停車費。透過借鑒相關文獻來識別關鍵屬性,資料收集集中在桃園市內壢火車站附近,該地區的條件符合環境和停車場管理同質性的研究假設。採用便利抽樣法,歷時3個月,涵蓋指定區域15個停車場。共收集了 740 個資料集,包括停車設施、每月需求、每月費用和停車場可用性資訊。透過情境分析產生並評估了 R2 值為 0.761 的多元迴歸方程式。該方程式根據經驗案例進行了驗證,證明其準確性在 95% 的置信區間內。;Residential properties frequently face challenges related to insufficient parking spaces, leading to the necessity of leasing external parking facilities to fulfill the residents′ parking requirements. Consumers are particularly attuned to the associated leasing costs, underscoring the need for a rational determination of monthly parking rates. This study aims to formulate and optimize monthly parking fees tailored to mixed residential areas in urban regions of Taiwan. By drawing on relevant literature to identify key attributes, the data collection focuses on the vicinity of Neili Train Station in Taoyuan City, where conditions align with the research assumptions of environmental and parking lot management homogeneity. A convenience sampling method was employed over a 3-month period, covering 15 parking lots in the designated region. A total of 740 datasets, including information on parking facilities, monthly demand, monthly fees, and parking lot availability, were collected. A multiple regression equation with an R2 value of 0.761 was generated and assessed through scenario analysis. The equation, validated against empirical cases, demonstrated an accuracy within a 95% confidence interval.
    Appears in Collections:[Executive Master of Construction Engineering and Management] Electronic Thesis & Dissertation

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