城市公共自行車使用量與建成環境的關係分析

Abstract

公共自行車作為城市公共綠色交通工具，因為其短期租賃、健康、便捷的特性，滿足了許多民眾短距離活動的需求，也減輕了一部分環境污染並緩解了城市中其他交通運具的交通負擔。但在提供給大眾便利的同時也帶來了資源分配不均、過度使用或過少使用這類的現實問題，所以關於公共自行車使用量的影響因素的研究可以幫助我們更好地瞭解在不同的環境下公共自行車的使用量，進而更好地規劃和分配停車樁和車輛的數目。過往對於公共自行車的研究，比較多集中在基本歷史發展、選址方案和時空分佈等課題，但對於使用者、自然條件和交通條件等影響因數，以及對應的資料時間跨度不夠全面的問題，過往的研究相對較少。 本論文的研究目的是瞭解建成環境對於公共自行車使用量的影響，以美國紐約市的花旗公共自行車作為研究案例，使用2017年1月至12月完整一年內之每一筆租借資料進行分析，找到各個建成環境因數對於使用量影響的強弱關係。首先透過文獻回顧，探討以往的建成環境對於公共自行車使用的影響因數，而後取得民眾使用公共自行車騎行的時空資料，並借助政府的開放資料取得建成環境所需之資料；接著整理和總結文獻回顧中所提出之影響因數提出假說，並經由地理資訊系統的空間分析的方法構建建成環境模型並將資料賦予各個公共自行車站點，以多元回歸的方式對不同建成環境下的自行車使用量進行分析，發現對於公共自行車的使用，建成環境變數中，商業區面積、主要道路長度、自行車道路長度、附近地標建築數量、距離學校距離、興趣點個數、1200公尺內公共自行車站個數和停車場個數會帶來大幅度的正向影響，居住密度、附近高中個數、自行車道路長度和距離公交車站距離則是小幅度的正向影響，而附近的長椅的分佈會帶來負向影響作用。另外，在天氣控制變數中的氣溫會對使用量產生正向影響，降雪和風速大小則會帶來負向影響作用。

Public bicycles, as urban public green transport, have met the needs of many people for short-distance activities because of their “short-term lease”, health, and convenience characteristics, and also reduced the problem of some environmental pollution and the traffic burden of other transport vehicles in the city. In the previous studies of public bicycles, most of them were focused on basic historical development, site selection plans, and spatial and temporal distribution. The impact factors were concentrated on users, natural conditions, and traffic conditions. There were factors for consideration and selection. The time span of the user profile is not comprehensive enough. The purpose of this research study is to understand the impact of built environment on the use of public bicycles, using the Citi Public Bicycle in New York City as a research case, and using each rental data from January to December 2017 to analyze, find The impact of each built environmental factor on the impact of usage. First, through a literature review, the impact of the built environment on the use of public bicycles was investigated. Then, spontaneous geo-information was used to obtain the space-time data of citizens using public bicycles to ride and use the government's open data to obtain the information needed for the built environment. Then, after collating and summarizing the impact factors proposed in the literature review, we put forward a theoretical hypothesis and built a built environment model through spatial analysis of geographic information systems and assigned the data to various public bicycle sites in a multiple regression approaches to different built-up environments. The amount of bicycle use was analyzed and its important impact factors were found. Finally, the theoretical hypothesis was verified and the impact of the built environment on the use of public bicycles was explored.