隨著近年來共享自行車系統的快速發展，影響借還次數的因素為何及影響程度的大小是一重要課題。過去的研究指出，除了外生變數的考量（例如:天氣、時間或建成環境變數等）外，還應充分考慮共享自行車系統的時空交互作用。
本研究依循Faghih-Imani and Eluru (2016) 對紐約共享自行車系統需求之研究，以台南市共享自行車系統T-bike為例，分析時空交互作用帶來之影響。研究使用空間面板數據估計時空交互作用，除採用空間滯後及空間誤差兩種空間作用之模型外，也針對某個站點某小時借還車數是否受前一小時、前一天同一小時及上週同一小時借還車數的影響 （此為時間滯後因子），以及某個站點某小時的借還車數是否受鄰近站點前一小時、前一天同一小時與上周同一小時的借還車數影響（此為時空滯後因子）進行分析。
研究結果顯示T-Bike站點間的使用情況具有時空交互作用，除了相鄰站點之借還車會受觀測站本身之借還車影響外，觀測站本身也會受本身站點與最鄰近站點前時間段之借還車影響，其中本身站點前時間段帶來的影響是以前一週同一小時借還車的影響最大，最鄰近站點前時間段帶來的影響則是前一小時借還車的影響最大，可從中看出T-Bike的使用模式存在的規律性，此外在分析結果中發現了影響共享自行車需求的重要因素（時段、火車站等），綜合以上模型分析的結果，我們建議可以利用觀察出的使用模式，針對不同卡別之使用者提出不同的行銷方式，像是可對悠遊卡會員在週末時提供景點周邊T-Bike站點的使用優惠等又或是針對火車站附近的道路規劃提出改善（建置自行車道、自行車交通號誌等）以促進民眾對於此系統的需求。

With the rapid development of bicycle-sharing systems (BSSs) in recent decades, many studies have been aimed toward determining the factors that affect the demand for BSSs. Past studies have pointed out that in addition to consideration of exogenous variables (such as weather, time, or built environment variables, etc.), the spatio-temporal interactions of BSSs should also be fully considered. For this reason, our research incorporates spatio-temporal interaction into the model building process.
In the present study, a spatial panel model is used to estimate spatio-temporal effects (including observed and unobserved) in order to analyze the data for Tainan City’s BSS (T-Bike). In addition, temporally lagged dependent variables and temporally and spatially lagged dependent variables are added. For example, the bike borrowing/returning rate in a specific time period for the station under observation may be affected by the bike borrowing/returning rate in the previous period for that station as well as neighboring stations. Ignoring the existence of these effects may lead to estimation errors in the model.
The research results provide strong evidence that T-Bike usage has a positive dependency and that there is also temporal and spatial interaction between BSS site borrowing and returning patterns. In addition, factors contributing to the demand for BSS were also found. A summary of the analytical results is provided; some reasonable explanations for the results are given, and reference suggestions for the future development and planning of the BSS are made. In addition, the results of the comparison between the models will also be provided to scholars who need to use the spatial panel model for analysis in the future as a reference.

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