为提高互联网租赁自行车管理的精细化和动态性，充分发挥互联网租赁自行车在城市出行中的作用，利用北京市互联网租赁自行车骑行大数据，运用统计学方法和ArcMap软件对骑行特征进行时空间分析和可视化，并通过对热点区域骑行规律的深入分析，挖掘不同功能区的需求特征。结果表明，互联网租赁自行车的使用具有明显的时空规律。一是互联网租赁自行车出行需求在工作日和非工作日具有较大差异，工作日骑行总量普遍高于非工作日，且逐时骑行量变化呈M形分布，具有明显的早晚高峰。二是工作日骑行热点主要集中在商务办公区以及大型商务区、居住区附近的地铁站点。三是商务办公区、居住区和公共交通站点三类不同功能区用车和停车需求具有不同的规律。因此，应建立基于大数据的规划模型并在相关城市及交通规划中充分考虑互联网租赁自行车出行需求，同时通过政企合作对互联网租赁自行车进行精准动态管理。
In order to increase the delicacy and dynamics of shared bike management, and give full play to the role of shared bike in urban travel, statistical methods and ArcMap software were used to conduct spatiotemporal analysis and visualization of the riding characteristics of shared bike based on the big data of Beijing users. Through the in-depth analysis of riding rules in hot areas, the demand characteristics of different functional areas were explored. Research shows that the use of shared bike has obvious time and space patterns. Firstly, there is a big difference on shared bike travel demand between weekdays and rest days. The total trip amount in weekdays is generally higher than that on rest days. The change curve for trip amount per hour in weekdays is M-shaped with morning and evening peak. Secondly, the cycling is concentrated in business office areas and subway stations near the business office and residential areas in weekdays. Thirdly, the using and parking demand in business office areas, residential areas, public transportation stations are different. Therefore, it is necessary to establish a planning model based on big data and the travel demand of shared bike should be fully considered in relevant urban and traffic planning. Governments and enterprises should cooperate to carry out refine and dynamic management on shared bike.
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