基于土地利用及客流特征的地铁车站分类

Classification of Subway Stations Based on Land Use and Passenger Flow Characteristics

为研究地铁站的精细化分类问题,利用基于出行链分析的通勤出行识别方法筛选通勤客流,结合早晚高峰的进出站客流量,识别车站的职住功能特征;基于百度地图开源平台抓取POI(Point of interest)数据,从用地功能角度进行组合归类,得到细粒度的车站周边土地利用特征。结合以上两类特征,建立基于非监督学习K-Means++方法的地铁车站分类模型,将北京地铁307个车站分为7类。根据其客流和周边用地特征分别识别为配套设施开发完善的典型居住型车站,具有商业开发潜力的典型居住型车站,配置一定工作岗位的居住型车站,高度开发的典型工作型车站,职住结合的工作型车站,旅游休闲型的车站,以及尚待开发的远郊车站。经过分析,该分类结果与实际情况高度吻合,验证了模型的有效性,可以为城市规划及车站周边土地开发提供依据。

This paper studies the fine classification of subway stations.In this paper,we use a commuter trip identification method based on trip chain analysis to identify the commuter passenger flow,then analyze the job housing characteristics of stations with the inbound and outbound passenger flow volume in the morning and evening peak.The point of interest(POI)data captured from the open-source platform of the Baidu map is classified according to the land use function to explore the built-up environment characteristics around the stations.Combined with the two types of characteristics,a subway station classification model based on the unsupervised learning k-means++method is established,and 307 stations of Beijing subway are divided into 7 categories.According to the characteristics of passenger flow and surrounding land use,the stations are classified as the typical residential station with well developed supporting facilities,the typical residential station with commercial development potential,the residential station configuring certain jobs,the highly developed typical working station,the working station combined with work and housing,the tourist and leisure station and the outer suburban station to be developed.The classification results are highly consistent with the actual situation,which verifies the effectiveness of the model,and can provide the basis for city planning and land development around the station.