基于站点爬虫数据的公共自行车系统时空特征分析

把握公共自行车使用的时空分布特征是优化公共自行车系统运行的前提.通过网络爬虫技术获取站点数据,定义了站点活跃度等指标,利用Dunn指数和Davies-Bouldin指数确定有效的站点活跃度聚类算法,引入全局Moran′s统计量和局部Moran′s统计量对站点使用情况进行空间统计分析,深入挖掘站点使用状况的时空分布特征.在对苏州市公共自行车系统的案例研究中,根据活跃度变化将站点聚为四类,发现一个站点的活跃度与周边13个(晚高峰)至20个(早高峰)以上的站点存在正的空间相关关系,可推测早晚高峰用户的平均骑行距离分别为2.2 km和1.7 km.研究结果还证实,虽然大部分站点的高峰期车桩比在空间上呈随机分布,但高车桩比站点分别聚集在几个不同的地区,低车桩比站点则集中出现在较大范围内,系统地揭示了站点间协调配合存在的问题. 

Spatial-temporal Characteristics of a Shared Bicycle System Based on Web Crawler Data

It is a prerequisite to optimize the operation of a shared bicycle system by collecting spatial-temporal characteristics of it.A web crawler technology is used to obtain data of stations.After that, indices like activity score (AS) are defined to measure operation of the system.In order to identify a valid cluster algorithm for computing AS, several methods are compared by using Dunn index and Davies-Bouldin index.Global and local Moran's statistics are introduced to analyze spatial-temporal characteristics of stations usage.In a case study of a shared bicycle system in Suzhou, stations are clustered into four types according to the changing regularity of AS.It is found that activity of one station has positive spatial correlation with 13 stations during rush hours in evening and 20 stations during rush hours in morning.It is speculated that average riding distance are 1.7 km and 2.2 km, respectively.Although majority of the normalized available bicycles (NABs) at peak times are spatially distributed at random, this study still finds that stations with high NAB are gathered in several different areas.Stations with low NABs are centrally located in a larger area in the city.The results also clearly reveal existing problems of coordination between stations in the system.