Abstract: To detect the movement of moving objects (vehicles) in real-time fashion,every moving object continuously reports its latest positions to the sever and the server keeps the data to answer queries posed by users,which can incur great communication cost and pressing overload on the server.In particular,this method cannot effectively detect the group movements and track the individual objects simultaneously.Therefore,this paper proposes a framework of double-granularity movement detection based on spatial-temporal anchors (DMDSA),which can effectively detect group movement and track individual positions by embedding each moving object into a spatial-temporal grid and reporting its movement pattern to servers whenever the moving object passes the anchor of each grid cell.During the offline stage,movement patterns of each grid cell are mined from historical trajectories by the server.During the running of moving objects,the server detects the group movements using the maximum-likelihood estimation by aggregating the mined movement patterns.Furthermore,independent-anchor policy and sequenced-anchor policy are used to identify the most likely running path,thus tracking each moving object in real-time.The experimental results on synthetic and real data sets demonstrate that DMDSA framework can not only detect the group movement effectively but also track individual object precisely with the great reduction of communication cost.

Key words: Group movement, Location reporting, Moving object, Position tracking, Road-network