Destination prediction is an essential task in a variety of mobile applications. In this paper, we optimize the matrix operation and adapt a semi-lazy framework to improve the prediction accuracy and efficiency of a state-of-the-art approach. To this end, we employ efficient dynamic-programming by devising several data constructs including Efficient Transition Probability and Transition Probabilities with Detours that are capable of pinpointing the minimum amount of computation. We prove that our method achieves one order of cut in both time and space complexity. The experimental results on real-world and synthetic datasets have shown that our solution consistently outperforms its state-of-the-art counterparts in terms of both efficiency (approximately over 100 times faster) and accuracy (above 30 increase).
Our work relies solely on the historical trajectory dataset to predict destinations, which is notably different from most previous work @cite_10 @cite_17 @cite_4 @cite_8 @cite_0 (i.e. no other information such as time or user profiles is included). This general setting allows us to analyze user movement when such knowledge is not available, which is often the case.
@cite_4 @cite_8 @cite_0 @cite_10 @cite_17
1982838671 1997458143 2062231365 1998451808
Location is a key context ingredient and many existing pervasive applications rely on the current locations of their users. However, with the ability to predict the future location and movement behavior of a user, the usability of these applications can be greatly improved. In this paper, we propose an approach to predict both the intended destination and the future route of a person. Rather than predicting the destination and future route separately, we have focused on making prediction in an integrated way by exploiting personal movement data (i.e. trajectories) collected by GPS. Since trajectories contain daily whereabouts information of a person, the proposed approach first detects the significant places where the person may depart from or go to using a clustering-based algorithm called FBM (Forward-Backward Matching), then abstracts the trajectories based on a space partitioning method, and finally extracts movement patterns from the abstracted trajectories using an extended CRPM (Continuous Route Pattern Mining) algorithm. Extracted movement patterns are organized in terms of origin-destination couples. The prediction is made based on a pattern tree built from these movement patterns. With the real personal movement data of 14 participants, we conducted a number of experiments to evaluate the performance of our system. The results show that our approach can achieve approximately 80 and 60 accuracy in destination prediction and 1-step prediction, respectively, and result in an average deviation of approximately 60 m in continuous future route prediction. Finally, based on the proposed approach, we implemented a prototype running on mobile phones, which can extract patterns from a user's historical movement data and predict the destination and future route. Destination prediction is an essential task for many emerging location-based applications such as recommending sightseeing places and targeted advertising according to destinations. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, almost all the existing techniques use various kinds of extra information such as road network, proprietary travel planner, statistics requested from government, and personal driving habits. Such extra information, in most circumstances, is unavailable or very costly to obtain. Thereby we approach the task of destination prediction by using only historical trajectory dataset. However, this approach encounters the "data sparsity problem", i.e., the available historical trajectories are far from enough to cover all possible query trajectories, which considerably limits the number of query trajectories that can obtain predicted destinations. We propose a novel method named Sub-Trajectory Synthesis (SubSyn) to address the data sparsity problem. SubSyn first decomposes historical trajectories into sub-trajectories comprising two adjacent locations, and then connects the sub-trajectories into "synthesised" trajectories. This process effectively expands the historical trajectory dataset to contain much more trajectories. Experiments based on real datasets show that SubSyn can predict destinations for up to ten times more query trajectories than a baseline prediction algorithm. Furthermore, the running time of the SubSyn-training algorithm is almost negligible for a large set of 1.9 million trajectories, and the SubSyn-prediction algorithm runs over two orders of magnitude faster than the baseline prediction algorithm constantly. The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data.This side eect provides the opportunity for innovative methods that analyse the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three dierent best matching methods to classify a new moving object are proposed and their impact on the quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements. In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns. This measures were tuned on a real life case study. Finally, an exhaustive set of experiments and results on the real dataset are presented. The collection of huge amount of tracking data made possible by the widespread use of GPS devices, enabled the analysis of such data for several applications domains, ranging from traffic management to advertisement and social studies. However, the raw positioning data, as it is detected by GPS devices, lacks of semantic information since this data does not natively provide any additional contextual information like the places that people visited or the activities performed. Traditionally, this information is collected by hand filled questionnaire where a limited number of users are asked to annotate their tracks with the activities they have done. With the purpose of getting large amount of semantically rich trajectories, we propose an algorithm for automatically annotating raw trajectories with the activities performed by the users. To do this, we analyse the stops points trying to infer the Point Of Interest (POI) the user has visited. Based on the category of the POI and a probability measure based on the gravity law, we infer the activity performed. We experimented and evaluated the method in a real case study of car trajectories, manually annotated by users with their activities. Experimental results are encouraging and will drive our future works.