考虑上下客点与POI关系的网约车临时停靠点研究

Study on the temporary parking point of online car hailing considering boarding point and POI

针对当前网约车数量急剧增加的现状,为避免网约车在商圈周围随意停靠造成交通拥堵,引导网约车停靠在预先划分的区域将缓解交通压力,因此对网约车临时停靠点的选址策略进行深入研究。利用滴滴出行成都市局部区域网约车公开数据集和城市热门POI数据,使用K-means聚类算法,分析POI数据和网约车上下客点数据之间的耦合关系,从而提出网约车临时停靠点选址策略。研究表明:网约车上客需求与附近POI数据密度正相关,即POI密度越大,网约车乘车需求越大。综上提出的结合上下车需求和POI聚类的网约车临时停靠点的选址方法,可以为减轻交通拥堵提供数据支持,具有实际意义。

In view of the rapid increase in the current number of online car hailing,in order to avoid the traffic congestion caused by the random parking of online car hailing around the business district,guiding online car hailing to park in the pre divided area will ease the traffic pressure.Therefore,the site selection strategy of online car hailing temporary stops could be studied in depth.Using the public data set of Didi Travel's local regional network taxi hailing in Chengdu and the city's popular POI data,K-means clustering algorithm is used to analyze the coupling relationship between POI data and online taxi boarding and alighting point data,thus proposing the location strategy of online taxi hailing temporary stops.The research shows that the demand for online car hailing is positively related to the nearby POI data density,that is,the greater the POI density is,the greater the demand for online car hailing is.To sum up,the proposed site selection method for temporary bus hailing stops based on boarding and alighting requirements and POI clustering can provide data support for reducing traffic congestion,which is of practical significance.