臺南市建城於1624年，為我國都市發展最早建立的城市，多數行政區仍保留歷史街廓與道路紋理，形成獨特的城市風貌。近年人口與經濟持續成長，私有運具使用率亦居高不下，導致停車供需失衡成為城市管理重要課題。為改善此問題，臺南市政府自2018年起推動「臺南好停NO.萬」計畫，積極擴大停車供給，其中以可及性高、成本低的路邊停車格最為廣泛使用。然而，路邊停車格的管理除了現行法規與收費制度亟需檢討外，亦需針對土地使用進行科學化調整，這是國內各級政府需重視的重要課題。本研究利用臺南市政府提供的2023年全年度停車收費資料，應用二項次分配模型結合馬可夫鍊蒙地卡羅（MCMC）模式進行參數模擬，探討路邊停車格周邊土地使用特性與路邊停車時數的關聯性。研究結果顯示，特殊地標、公共設施以及學校、醫療設施等土地使用，對路邊停車格使用時數呈現正向顯著的影響，未來宜針對該類型土地使用周邊停車格存廢審慎評估，同時在設施開發過程中應審慎考量停車需求外溢問題。而商業土地影響使用路邊停車使用時數亦有正向影響，尤其是小型商業單元，可針對其週邊停車費率進行更嚴格的管制策略；住宅區則減少鄰近路邊停車格使用時數，未來可評估釋放路邊停車空間做其他有效運用。路外停車場在有效吸引路邊停車使用行為的轉移，致使周邊路邊停車格之時數有顯著降低。值得注意的是，公共運輸服務對路邊停車格使用時數的影響，與過去研究結果存在差異，顯示臺南市在促進公共運輸使用率的政策上，仍有成長空間。本研究將二項次分配與MCMC法應用於路邊停車使用時數與土地使用間的互動關係，在模型收斂性與參數估計上表現良好，模型可作為管理者量化各類土地使用對路邊停車時數影響，為停車格劃設、差別費率設定與政策制定提供依據。然而，鑑於路邊停車使用行為受到多種內外生變數影響，未來可進一步優化模型參數設計，結合不同機率密度函數與變數調整，以提升整體可靠度與準確性。

Tainan City, established in 1624, is one of Taiwan’s earliest urban developments, with most districts retaining historical street patterns and forming a unique urban landscape. Rapid population and economic growth, coupled with high private vehicle usage, has created a persistent imbalance between parking supply and demand. To address this, the city government has implemented policies to expand parking supply, with on-street spaces being the most accessible and widely used.
This study uses 2023 full-year parking data and applies a binomial sub-distribution model combined with Markov Chain Monte Carlo (MCMC) simulations to examine the relationship between land use and on-street parking performance. Results show that landmarks, public facilities, schools, and medical institutions positively influence parking usage, while residential areas reduce performance. Small-scale commercial units also increase usage, suggesting stricter local fee control, whereas off-street parking effectively diverts demand, reducing nearby on-street usage. Public transportation effects differ from previous studies, indicating potential for policy improvement.
The model demonstrates good convergence and parameter estimation, providing a quantitative tool for assessing land-use impacts on on-street parking and informing space allocation, pricing strategies, and policy decisions. Future studies may further refine model parameters and incorporate additional factors to improve reliability and accuracy.

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