都市公共自行車騎乘路線推估

為了改善過去過於依賴私人運具的交通規劃侷限，公共自行車系統愈來愈受到重視。公共自行車作為短程交通工具，可減少私人載具的使用、都市交通擁擠、環境污染及能源損耗等問題。在臺灣則以臺北市的「YouBike微笑單車」發展最為成熟。雖抱持如此願景，但在臺北市以公共自行車作為日常通勤運具的比例仍然不高，並且自行車常會與其他交通工具產生排擠、爭道等衝突，甚至自行車彼此之間也會有衝突，在不知道公共自行車到底騎在哪裡、缺乏實際了解使用者的行為模式與需求之情況下，相關單位在規劃時並不知道何處是最需要被優先處置的，因此推估公共自行車騎乘路線，並了解整體公共自行車路網，哪裡是騎乘熱區，此不僅對政府單位的資源投資與基礎設施的規劃得以投入於最需要的路段，對於使用者也在路線選擇上有十分重要的參考價值。但目前由於公共自行車資料上的限制，僅具有借還的起訖站點，以及租借時間，缺乏實際的騎乘路線資料，在研究上十分困難，過去研究多以場站站點為研究主體，缺乏都市交通路網的空間尺度。因此本研究將路網分析納入公共自行車研究中，並選擇臺北市大安區作為研究區，以2015年5月至2018年5月臺北市YouBike的逐次租借資料，運用K最短路徑方法，得出公共自行車於通勤尖峰時段之路線，並更進一步計算都市內之公共自行車路網。研究結果發現若與最短路徑計算之公共自行車路網相比，以K最短路徑計算之路網，可以反映更加詳盡、於巷弄之間的公共自行車騎乘路段熱區。在道路狹小、缺乏交通號誌的巷弄中，更需要被指認出何處為公共自行車騎乘熱區，以免發生危險。在學術理論上，突破無實際騎乘路線資料限制，以路網分析進行之外，本研究也跳脫過去以站點為主體的分析視角，以都市路網的空間尺度重新理解公共自行車；在實務上，得以供相關單位審視及參考，以利其資源的投資與規劃，也可提供民眾何處為自行車騎乘熱區，作為其路線選擇之依據，鼓勵民眾使用公共自行車作為通勤載具，連結都市交通的「最後一哩路」。

關鍵字

公共自行車系統；路網分析； K最短路徑演算法

並列摘要

In order to overcome the limitations of automobile-dependent transport planning, there has been increasing attention on public bicycle systems. Public bicycle systems are well known as short-distance transit vehicles, expecting to reduce the use of private vehicles and the traffic congestion, environmental pollution, energy consumption and other problems. In Taipei, Taiwan, YouBike is the well-developed public bicycle system. Although there is the expectation for YouBike, the percentage of using YouBike as a usual commuting mode in Taipei are still small. There are some conflicts not only between bicycles and other transportations, but between every bicycles. In this context, the competent authority can’t realize where are needed to implement policies first without recognizing the movement behavior of users of public bicycles and what paths they follow. As a result, it is essential to estimate public bicycle routes and realize whole public bicycle road network. Where are the hot spots of public bicycles? There are __for the government to invest resources and infrastructure in the road segments where they are needed, as well as users to choose alternative path. Because of the data limitation of public bicycles which lack the real routes data, not many studies have focused on the real routes of public bicycle systems. Furthermore, previous studies have focused on the station level, which have lacked the spatial scale from urban road networks. Therefore, this study aims to introduce the network analysis into research of public bicycle systems. This study chooses Da’an District, Taipei City as the study area, and employs all of the rental records of YouBike from May, 2015 to May, 2018. K-shortest path algorithm is used to analyze routes of public bicycles in peak hour, and explore the public bicycle networks within the city. This study contributes to recognize the more detailed hot spots in alleys. Because it is very dangerous in the alleys which are narrow and lacking in traffic signs, recognizing the hot spots in alleys is very vital to avoid the accident. Theoretically, this study not only uses the network analysis to overcome the data limitation, but also realizes public bicycle systems from different spatial scale; in practice, it can be beneficial for government to plan where are needed to invest resources, and for users to choose the safer, more comfortable path. Therefore, citizens can be encouraged to use public bicycles as commuting vehicles, and to build the "last mile" connection to the urban transportation.