隨著近年共享經濟的蓬勃發展，共享單車已經成為隨處可見的便利公共代步工具。目前單車數量的站點信息僅能提供目前該站點現有單車數量，但難以預測使用者到達該站時是否滿站/空站，從而造成借還車時等待時間的浪費。因此我們提出了一種基於威布爾分布的方法來預測單車未來短暫時刻的數量。我們針對共享單車在工作日使用習慣和特點，提出了基於空間上站點分類和時間上威布爾分布的共享單車站內數量的預測方法。此方法為使用預處理好的共享單車站點歷史數據，對站點單車的增量活躍區/減量活躍區進行劃分，再採用威布爾分布擬合活躍區的機率密度函數，因此可透過當前共享單車數量和機率密度函數，計算得出共享單車的預測數量。本方法並使用了全天預測結果的R-Square值進行評估，表現最佳站點之R-Square值為0.97134，次佳站點的R-Square值亦有0.83617，證實本方法的預測的準確性。

With the rapid development of the sharing economy in recent years, shared bicycles have become a convenient transportation tool everywhere. Currently the station can provide only the number of existing bicycles at the station, but this information can not be used to predict whether the station is full/empty when the user arrives. This might result in a long waiting time when users borrow or return the bicycles. Therefore, we propose a prediction method based on Weibull distribution to predict the number of bicycles in the short-term future. Aiming at the usage habits and characteristics of shared bicycles during workdays, this prediction method for the number of shared bicycles is based on the classification of stations in space and Weibull distribution in time. This method uses the pre-processed historical data of shared bicycle sites to divide the incremental active area/decrease active area of the bicycles on the site, and then uses the Weibull distribution to fit the probability density function of the active area, so it can be shared through the current sharing The number of bicycles and the probability density function are calculated to obtain the predicted number of shared bicycles. The R-Square value of all-day forecast results is used for the method evaluation. Among all investigated stations, the best R-Square value is 0.97134 and the second one is 0.83617, which verify the prediction accuracy of our proposed method.

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