基于出行时空数据的分时租赁汽车与网约车出行场景比较研究

分时租赁和网约车同属共享汽车,但规模对比悬殊.找到差异化的出行场景有利于分时租赁在网约车主导的共享汽车市场中谋求立足之地.本文以北京地区某分时租赁公司2017年5月1日—30日的出行订单和2018年4月23日—29日的网约车出行订单为研究对象,结合城市兴趣点数据,利用地理信息层次聚类、关联规则等方法挖掘两共享汽车的典型...

Travel Scenes Comparison of Time-Sharing and Car-Hailing based on Traveling Spatiotemporal Data

Both time-sharing rental cars and car-hailing are car sharing service, but with different scales. Finding differentiated travel scenes is conducive to time-sharing in seeking a foothold in the car sharing market dominated by car-hailing. This research uses car sharing records data combined with city's points of interest (POI) data to analyze the spatial temporal characteristics, and typical travel scenes of time-sharing and car-hailing in Beijing. Firstly, hierarchical clustering method was used to define the most distinguished clusters of city's grid cells based on POI data. Dunn index examined the optimal cluster number. Secondly, Origin and Destination (OD) locations of each car-sharing trip were labelled by the cluster types. The users' preferences were observed from OD cluster pairs appearing more frequently than others in the records. Thirdly, typical travel scenes were extracted by analyzing association rules of these cluster pairs. In the end, spatiotemporal patterns of typical travel scenes were tested. The findings of this study can be divided into three portions. Firstly, car-hailing mainly serves commuters and travels among business districts within the city. Secondly, time-sharing mainly serves non-commuter travels, and the representative travel scenes are short-distance city travel for tourism and Midnight travel in suburban areas. Many of these observed relationships are interpretable. For example, a short-distance city travel for tourism usually lasts half a day. Renting behaviors avoid the morning and evening rush hours of commuting. A midnight travel in suburban areas happens outside the city center, which usually lasts less than an hour since public transport is not available during that time. Thus, this travel scene seems to be related with urgent travel demands. Thirdly, the cost of time-sharing is far lower than that of other alternative modes and constitutes only 30%~50% cost of others, indicating that car sharing is beneficial when compared with other modes in these scenarios obtained in our research. These findings can serve as references and suggestions for time-sharing's promotion and operation process. The travel scenes mining method proposed in this study can be repeated in other car sharing researches.