基于出租车轨迹数据的城市热点出行区域挖掘

摘要

出租车轨迹是蕴含着居民出行行为的地理时空大数据,从出租车轨迹数据中挖掘居民出行的热点区域和移动模式对于城市规划、交通管理等具有重要意义.针对现有热点区域挖掘方法在面对大规模轨迹数据时存在的伸缩性差、计算效率低等问题,提出一种基于网格密度的GScan聚类算法.该算法首先将轨迹空间划分成网格单元,并设定网格单元的密度阈值;然后将轨迹点映射到网格单元,基于密度阈值提取热点网格单元;通过合并可达热点网格单元发现城市的热点区域.以重庆市出租车轨迹载客/卸客点进行实例分析,给出网格单元大小和密度阈值2个参数的设定方法,得到重庆市主城区居民出租车出行热点区域的时空分布,进而分析重庆市居民出行行为.%Taxi GPS trajectories data contains massive spatial and temporal information of human activity and motility.By using a spatial clustering algorithm,attractive areas and moving patterns of people' s travel can be discovered from the taxi trajectory data,which is of great significance for urban planning,traffic management,and location-based services.Because of the poor scalability and low efficiency of mining attractive areas algorithm in the face of large scale trajectory data,we propose a new GScan clustering algorithm based on grid density.In this method,firstly,the grid cells are divided from the trajectory data space,then the spatial points are mapped to grid cells,the hot grid cells can be extracted by setting the threshold.At last,through merging reachable hot grid cells,the attractive areas in the city can be found.Based on taxis' pick-up/drop-off data of Chongqing,experiments and analysis are carried out.The parameters in the method are discussed,and a method of setting the parameters in the experiment is given.At the end of the paper,the spatial and temporal distribution of the attractive areas in Chongqing is presented to analyze the travel behavior of Chongqing citizen.