共享单车用户骑行起讫点时空特征分析

针对共享单车的供需失衡、分布不均问题，研究了共享单车用户骑行起讫点的聚集区分布以及不同区域的骑行时间特征，为共享单车的调度运营提供理论支撑。基于用户的骑行订单数据，采用均值漂移算法对骑行起讫点进行聚类学习，得到共享单车的骑行聚集区分布；随后采用spearman相关系数来衡量骑行时间特征的相似度，对不同骑行聚集区的借车与还车量的累计差值的时间序列曲线进行聚类处理，划分出6类典型的骑行特征，并对不同骑行特征所在地的兴趣点（POI）进行因子分析，结果表明:在空间上，共享单车的骑行聚集区的空间分布与所在区域的城市路网的布局形式存在较大关联，不同时间段的骑行聚集区的分布大致相同，仅在出行量上存在差异。骑行聚集区的骑行特征与土地利用性质之间存在相关性，例如，对于骑行特征为1天内借车量小于还车量的骑行聚集区，其主导因子为商业用地，占比为0.4；对于1天内用户的借车量大于还车量的骑行聚集区，其主导因子为住宅用地，占比为0.57。多种用地性质混合的区域，借还车的差值较小且易产生波动。此外，同一类型的骑行时间特征的主导因子占比在工作日与非工作日会产生变化，同一区域的骑行时间特征在工作日与非工作日存在差异。 

An Analysis of Spatial-temporal Characteristics of Origin and Destination of Shared-bike Users

In a view of the frequent imbalance between supply and demand and uneven distribution of shared bikes over space, this paper studies the origin-destination distribution of shared-bike users and the temporal characteristics of riding demand in different areas, so as to provide theoretical support for dispatch operations of shared-bike systems. Based on riding data of users, the mean-shift algorithm is used to cluster the origin and destination points of riding, and the distribution of areas with a high riding record is obtained. Then, Spearman correlation coefficient is used to measure the similarity of temporal characteristics of riding demand. Six typical temporal characteristics of riding demand are extracted by clustering the temporal cumulative differences between the volumes of rented and returned bikes in different areas. The relationship between temporal characteristics of riding demand and land use (represented by point of interest, POI)is studied by factor analysis. The results show that the spatial distribution of aggregation areas of shared bikes is basically correlated to the spatial pattern of the urban road network in the area. There is little variation for the distribution of aggregation areas in different time periods, and the only difference is the volume of bike riding in different areas. Besides, it shows that temporal characteristics of riding demand and land use are related. Commercial land use is the dominating factor for the areas where the number of rented bicycles is less than that of returned bicycles in one day, which accounts for 40% of the total. For the areas where the number of rented bicycles is larger than that of returned bicycles in one day, residential land use is the dominating factor, accounting for 57% of the total. In areas with mixed land use, the difference between bicycle renting and returning is small and prone to fluctuate. In addition, the proportion of dominant factors of a temporal characteristics of riding demand may change between weekday and weekend, and the temporal characteristics of riding demand in a region are different between weekday and weekend.