公共自行車系統是近代蓬勃發展的大眾運輸系統之一。具備主動出行的特性解決短距離旅次的需求，在都市中與其他運具相輔相成，輔助完成最後一哩路的交通出行，發展至今已經成為各大城市發展永續交通的重要政策。為管理龐大的租借系統，智慧型的工具IoT設備也被引入協助管理，相應產生許多旅次資訊，其產生的大量數據提供分析都市移動的重要依據，有助於研究都市中的旅運行為與個人移動的空間分布。
以旅次分佈理解的都市空間結構是常見的分析方式，過去對於旅次分佈的調查常受限於資料調查的繁瑣與局限，無法對都市的移動進行更細緻時空分布的調查。公共自行車的個體旅次資訊提供數據分析的重要依據。但大量旅次數據資料的處理是相當具有挑戰的，因為大量的資料中涵蓋許多雜亂的數值以及重疊的資訊，使得無法從中取得有價值的資訊。因此需要更有效率方法理解資料中所蘊含的訊息。過去的研究中對於都市結構的分析沒有標準的框架或程序，本研究的挑戰在於如何以現有的大數據資料探索都市活動空間分布的規律。
交通型態分析上，已經有相關研究以圖論或網絡結構的概念討論其中的關係，網絡結構並非單純只聚焦一個點討論交通量，而是觀察節點間的互動關係，在都市移動的分析上具備觀察分析土地與交通相互作用的潛力。但多數相關研究偏重於指標建立、探究移動的規律性或是加強預測，大部分缺乏將這些路網結構呈現的空間不對稱與實際的空間使用型態進行討論與比較。
本研究以臺北市作為實證分析地區，分析公共自行車的使用資料呈現的旅次型態，應用社會網絡的概念分析公共自行車旅次資料所呈現的都市活動型態，透過社群偵測的模矩度指標指認自行車站點的階層式組織結構。社群偵測建構的公共自行車使用型態社群內能反映出特定的都市活動與分佈。
研究成果得出在時間特徵下的臺北市公共自行車社會網絡分布圖，以邊界的形式討論時間差異產生的都市活動。另外研究結果顯現單一設施的空間分布與自行車使用型態的空間分布比較結果並不好。本研究有助於理解都市活動背後所反映的空間結構也加強對於自行車使用型態的分析，本研究分析旅次型態的框架可以作為都市空間規劃的基礎資訊。

The Public Bicycle System (PBS) has become an important policy for sustainable transportation in many big cities. The big data generated from PBS provides an important foundation for analyzing urban mobility, and helps to evaluate the spatial distribution of travel behavior and personal mobility in urban areas.
However, the processing of a large amount of trip data is challenging, because a large amount of data contains many cluttered values and overlapping information, making it impossible to obtain valuable information from it. Therefore, more efficient methods are needed to exploit the information contained in the data. There is no standard framework or procedure for the analysis of urban structure in the past research. The challenge of this article is how to use the existing big data to explore the spatial distribution of urban activities.
This research analyzes the travel patterns presented by public bike usage in Taipei City, Taiwan. Appling the concept of social network to analyze the urban activity patterns presented by the public bike travel data. The relationship between bike stations is identified through the modularity index detected by the community. The usage patterns of public bikes constructed by community detection can reflect specific urban activities and distribution.
The results conducted the social network distribution map of the public bikes in Taipei City under the characteristics of time and integrate the spatial structure differences of urban activities caused by time differences in the form of boundaries. Finally, this research proposed a method to compare the spatial structure presented by public bike usage with the spatial structure presented by points of interest (POI).
This study helps to understand the spatial structure of urban activities and strengthens the analysis of bike usage patterns. The framework of travel patterns can be used as basic information for urban spatial planning.

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