本研究針對能提供長時間停留或提供消費娛樂之場域所附設的停車場，對於增加充電服務將特定客群導流至特定場域，提升該場域的消費、人潮與營利之問題進行討論。在初期評估時，並無大量樣本資料以及實際充電需求之模型參數因時間或空間而有不同，較不適用於古典統計方法(即機率分配方法)， 因此，本研究提出使用貝氏推論法(Bayesian inference)模擬車主每次到達停車場的充電量需求機率，利用觀測資料估計具有個別差異之模型參數，從已知參數的先驗隨機機率分佈，再透過實際觀察到的數據作為條件機率，獲得事後機率分佈，可隨時依任一觀察證據的變動更新參數的分配，建立一個更符合實際所需的模式，並使用馬可夫鏈蒙地卡羅(Markov chain Monte-Carlo, MCMC)對後驗分配進行抽樣，計算出參數估計值。
本研究以探討需求特性出發，以充電裝數量作為決策變數，並利用貝氏推論的需求量後驗分配的期望值作為參數之一建構優化數學模型。為了表現該機率函數的隨機性與變異性，本研究使用Arena 16.0建立模型來模擬真實系統的運作，並設計各時段的進場車輛數，以及推導平均停放系統時間機率分配，透過模擬實驗可以得到系統改變的量化結果，探討裝置數量與服務水準之關係，因此可以根據經營者的需求做出適當的策略並且提升民眾購買電動車的意願。

In this paper, we apply the Bayesian computational methods to estimate the parameters of electric vehicles (EVs) charging demand at the place where supermarkets or restaurants offer their customers to use the parking area at free of charge. Unlike traditional Frequentist, Bayesian inference models uncertainty of a set of parameters in target distribution by a probability distribution and makes inferences depends on subjective prior distributions that may vary from one investigator to another. Therefore, it has more stochastic, variant and small sample size properties. For a parking area with charging infrastructure, the amount of time drivers spending there relies on their charging demand that is target distribution in this paper. We also illustrate the application in the Arena simulation, which can identify the possibility of the system performance. The posterior distribution of charging demand is applied to simulate stochastically and realistically the amount of time EVs stay in the system. The result will yield the estimates of service rate indicating the number of processing and departure rates. For the purpose of this study, the providers are allowed to determine the minimum charger required to meet a pre-specified service level at parking lot.

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