隨著智慧城市與物聯網的快速發展，相關的研究議題逐漸受到全球許多產官學界的重視，其中一項熱門的智慧型運輸系統應用為公共自行車系統。公共自行車系統是一種能讓一般大眾於任何自動租賃站租借自行車，並在城市中進行短程旅行的服務。世界上有許多公共自行車系統，如巴黎的Vélib’、紐約的Citi Bike、以及臺北的YouBike。一個公共自行車系統要能成功，其重要因素在於好的使用者服務品質，也就是車站必須有足夠的自行車供租借、以及足夠的空車位可供還車，否則將會有使用者的需求無法被滿足。而此問題一般常用的解決方法是指派可載運自行車的貨車至各個車站填補過少或取走過多的自行車，因此預測使用者之借還量對於服務品質的提升是相當重要的，本研究使用遞迴神經網路來預測公共自行車系統中使用者之借還量。此遞迴神經網路包含了三個部分：週期、近期、以及最近時間點，它們分別代表過去不同的時間區間的歷史資料。我們將歷史的租借資料輸入進遞迴神經網路中，並經由迭代訓練後，可得到一組遞迴神經網路的最佳參數，往後只要將過去時間點的租借紀錄輸入進遞迴神經網路中，即可預測未來一天之自行車借還量。最後我們和Poisson的方法進行比較，實驗顯示我們的模型可以比它預測要來得精準。

As the rapid development of smart city and Internet of Things (IoT), related research issues have attracted much attention from industry and academia around the world, and Bicycle-Sharing System (BSS) is one of the thriving application of smart transportation system. BSS is a system that allows users to rent a bicycle from any automatic rental station, have a short travel around the city, and return it at any station. There’re many BSSs in the world, such as Vélib’ in Paris, Citi Bike in New York, and YouBike in Taipei. The key point for success of BSS is having a good quality of service for users, which means that the stations should have enough bicycles to be rented and free places for users to return the bicycles. If not, then it cannot satisfy all the users’ needs. The problem is usually solved by dedicated vehicles to rebalance the bicycles, that is, picking up the bicycles from the stations which have too many bicycles and unloading them to the stations having too few bicycles. Therefore, predicting the rental (i.e. the number of renting or returning bicycles) from users in the future is important to improve the service quality. This research uses Recurrent Neural Network (RNN) to predict the rental from users. The RNN consists of three parts: period, closeness, and general. Each of them represents the historical records in different time intervals in the past time respectively. We input the historical rental data into RNN, and after iterative training, we can obtain a set of optimal parameters of RNN. As a result, we can predict the bicycle rental in the coming day by inputting the rental records of the past time into RNN. Finally, we compare the effectiveness among this and the method of Poisson and prove that our model outperforms it.

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