臺北市公共自行車使用特性與空間分佈型態之趨勢變化分析

近年來遭受能源消耗與氣候變遷導致之環境衝擊，各國城市積極推動擁有低污染、低耗能、可及性高又兼具運動休閒特性等優勢之公共自行車系統的發展，而我國最為完善且較具規模者為臺北市「YouBike微笑單車」。許多系統營運至今已日漸成熟，掌握時空變化下使用者之租借需求，有效管理供需平衡以提高整體租借量，應為系統長期經營之關鍵。然而，現有關於公共自行車之研究大多透過短期橫斷面數據或以抽樣、問卷調查形式評估其使用特性及影響因素。是故，本研究目的在於運用臺北市YouBike系統自2016年1月至2021年5月之站點資訊與租借資料，瞭解長時間下使用者之租借情況與分佈型態，並考量縱向時間與空間差異概念，建構公共自行車使用需求趨勢模型，評估不同因素對使用量之關係與影響程度，從而提供系統營運規劃與策略建議。本研究首先爬梳文獻且歸納公共自行車有關議題及使用之影響因素，其次以統計分析、空間自相關分析，初步觀察使用量的時間變動趨勢及空間分佈型態變化。最後，根據歷年使用量、平日及假日使用變化量之縱貫性與橫斷面資料型態，分別以追蹤資料模型、全域型與地域型迴歸建構使用需求趨勢模型。實證結果顯示，歷年租借時間趨勢相似且大部分為短程使用，而熱門租借站點與騎乘路線大多鄰近捷運站和學校周邊。另外，公共自行車使用量之顯著影響因素為人口數、從業員工人數、站點數量、假日與降雨天數，而使用變化量之顯著影響因素則為人口數、與捷運站點距離及站點數量。尤其從地域型迴歸模型結果得知，不同變數考量空間特徵下之影響程度和顯著性檢定皆有所差異，且應用多尺度地理加權迴歸可更有效地解釋空間變異下不同因素對於公共自行車使用之影響，據此提出系統站點佈局、容量調整、車輛調度以及自行車道路網規劃等相關策略建議，以期提升整體公共自行車使用量。

關鍵字

公共自行車系統；大數據；空間自相關分析；地理加權迴歸；多尺度地理加權迴歸

並列摘要

In recent years, due to the environmental impact caused by energy consumption and climate change, various cities have actively promoted the development of public bike systems with the advantages of low pollution, low energy consumption, high accessibility and both sports and leisure characteristics. "YouBike" in Taipei City is the most developed and large-scale one. As many systems have become more mature in operation, it should be the key to the long-term operation of the system to grasp the rental needs of users under the change of time and space, and effectively manage the balance between supply and demand to improve the overall rental. However, most of the existing studies on public bicycles evaluate their usage characteristics and influencing factors through short-term and cross-sectional data or in the form of sampling and questionnaire surveys. Therefore, the purpose of this research is to use the site and rental data of the Taipei YouBike system from January 2016 to May 2021 to understand the rental situation and distribution patterns of users over a long period of time. Furthermore, the study considered the concept of longitudinal time and space difference to construct a demand trend model for public bicycle use, and to evaluate the relationship and influence of different factors on the use of bicycles, so as to provide system operation planning and strategic suggestions. The study firstly reviewed the literature and summarized the issues of public bicycles and the influencing factors of the use, then used statistical analysis and spatial autocorrelation analysis to preliminarily observe the temporal trends and spatial distribution patterns of bicycle usage. Finally, according to the longitudinal and cross-sectional data of usage over the years, usage changes on weekdays and holidays, the panel data model, global and regional regressions were used to construct usage demand trend models. The results showed that the trend of rental time over the years is similar and most of them are used for short distances, while popular sites and riding routes are mostly near MRT stations and schools. In addition, the significant factors affecting the use of public bicycles are the population, the number of employees, the number of stations, the number of holidays and rainy days. The significant factors affecting the usage changes are the population, the distance from the MRT station, and the number of stations. Especially from the results of the regional regression model, it is known that the degree of influence and significance test of different variables considering spatial characteristics are different, and the application of multiscale geographically weighted regression can more effectively explain the effect of different factors on the use of public bicycles under spatial variation. Based on this, the relevant strategic suggestions on system site layout, capacity adjustment, vehicle scheduling, and bikeway planning are proposed, in order to increase the overall use of public bicycles.