現代都市交通系統中，公共自行車系統佔有重要地位，並在緩解交通擁堵和環境保護等方面發揮了顯著作用。故本研究旨在測試動態模態分解在分析公共自行車租借量上的效果，並瞭解公共自行車租借量在時空上的結構。過往針對公共自行車旅次時空特性的研究多因為將時間與空間視為兩條互不相干的軸線。綜觀先前的研究可以發現，以往研究實際旅運資料的方法在空間上與時間上涇渭分明，通常會先將地理空間編定，以分區內最顯著的指標來替分區命名或解釋，甚少有方法能整合時間與空間於同一模型下進行分析。而動態模態分解可同時從時間與空間兩種面向，解構公共自行車租借量在各種分層模態之組成。
本研究以臺北市為研究範圍，使用 2023 年 3 月 6 日到同年 3 月 10 日之公共自行車使用資料，整理為時間—站點矩陣進行分析。首先利用動態模態分解的方法，分解公共自行車旅次時空動態結構之組成，以從時間與空間的軸向反映都市活動時空結構，並且利用核密度分析的方法，對分解出的都市活動時空結構進行視覺化解釋，之後分為全臺北市以及內湖區兩個尺度的視角，利用 K-平均演算法與調整蘭德指數找尋模態間的相似性，接著利用調整蘭德指數與 Z 分數輔以外生的環境資料，判斷模態與那些地理現象或社會現象的空間分佈較為相近，再將類似的模態組合。
最終本研究建立了一個描述公共自行車租借量的動態模態分解模型，研究結果發現，許多的模態公共自行車租借量較大的站點，在空間上具有空間群聚性，其在捷運站的周圍有分解出較明顯的租借量，並透過模態的重疊組合，可還原出捷運站附近公共自行車站點租借量時間分布之大多數特徵。本文的創新與貢獻在於，探討公共自行車借還的旅次特性，如何反映在旅次時空動態結構的群聚性上，並實際測試動態模態分解在都市交通運輸上的應用，並提出使用上的相關心得。

This paper explores the application of dynamic modal decomposition methods in analyzing public bicycle rental volume, aiming to understand its spatial and temporal structure. The study took Taipei City as the scope, used the rental data of the YouBike 2.0 system, and analyzed the rental volume from March 6 to 10, 2023, after excluding the influence of factors such as rainfall and holidays. Through the dynamic mode decomposition method, the study decomposes the spatiotemporal dynamic structure of public bicycle rental volume, and uses kernel density analysis and K-means algorithm to further understand the similarities between modes. The research results show that the time dynamics of most modes are mainly composed of sine waves with specific periods, and the rental volume near subway stations shows obvious cyclical changes. Research shows that dynamic modal decomposition has potential in urban big data analysis, but further empirical research is needed to improve its accuracy.

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