Personalized Top-k Trajectories Search Based On Location

The proliferation of mobile devices and the development of location-based services(e.g.,GPS),which generate and collect a large number of trajectories.Conventional location-based trajectory search in which context the query is only a small set of locations with or without an order specified,while the target is to find the Top-k trajectories from a database such that trajectories can be as close as possible to all locations geographically.However,retrieved trajectories of this way can’t go through all locations as close as possible,because it’s limited by trajectory itself.In addition,retrieved trajectories cannot satisfy users,because the travel time is too long from trajectory to all locations.Based on the above issues,this paper extends the conventional location-based trajectory search,considering two factors of travel time and splicing trajectories,we propose the personalized location-based trajectory search.First of all,location-based splicing trajectories pair search is proposed based on the idea that trajectory can spliced.This method can return trajectory pairs,and with the help of trajectory pairs,users can get trajectory which is closer to all the query points than that returned by conventional location-based trajectory search.With this method,transformation from one trajectory to another trajectory can be achieved,and the influence of trajectories with non uniform distribution on search is reduced.For the termination of search process,the upper bound and the lower bound are defined and an effective termination condition of the search process of splicing trajectory pairs search is given.Next,most of the trajectory search only focus on spatial information,but ignore traffic conditions and travel time.This means only the shortest distance on geography is considered to be the optimal trajectory.This paper proposes spatial-temporal domain personalized trajectory search by location.On the one hand,it pays attention to the significance of every query point to the user and assigns a weight to every query point,on the other hand,we redefine the distance function of trajectory to query: It also considers the travel time in temporal domain and the distance in spatial domain,which makes users satisfied with travel time and distance.For candidate validation,upper bound and lower bound are used to prune trajectories,to terminate search efficiently.At last,on the real data set,two algorithms proposed in this paper are compared with traditional trajectory search,which verifies the efficiency of the two algorithms in this paper.