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).
At the heart of Air Traffic Management (ATM) lies the Decision Support Systems (DST) that rely upon accurate trajectory prediction to determine how the airspace will look like in the future to make better decisions and advisories. Dealing with airspace that is prone to congestion due to environmental factors still remains the challenge especially when a deterministic approach is used in the trajectory prediction process. In this paper, we describe a novel stochastic trajectory prediction approach for ATM that can be used for more efficient and realistic flight planning and to assist airspace flow management, potentially resulting in higher safety, capacity, and efficiency commensurate with fuel savings thereby reducing emissions for a better environment. Our approach considers airspace as a 3D grid network, where each grid point is a location of a weather observation. We hypothetically build cubes around these grid points, so the entire airspace can be considered as a set of cubes. Each cube is defined by its centroid, the original grid point, and associated weather parameters that remain homogeneous within the cube during a period of time. Then, we align raw trajectories to a set of cube centroids which are basically fixed 3D positions independent of trajectory data. This creates a new form of trajectories which are 4D joint cubes, where each cube is a segment that is associated with not only spatio-temporal attributes but also with weather parameters. Next, we exploit machine learning techniques to train inference models from historical data and apply a stochastic model, a Hidden Markov Model (HMM), to predict trajectories taking environmental uncertainties into account. During the process, we apply time series clustering to generate input observations from an excessive set of weather parameters to feed into the Viterbi algorithm. Our experiments use a real trajectory dataset with pertaining weather observations and demonstrate the effectiveness of our approach to the trajectory prediction process for ATM. 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. Human behavior is often complex and context-dependent. This paper presents a general technique to exploit this "multidimensional" contextual variable for human mobility prediction. We use an ensemble method, in which we extract different mobility patterns with multiple models and then combine these models under a probabilistic framework. The key idea lies in the assumption that human mobility can be explained by several mobility patterns that depend on a sub-set of the contextual variables and these can be learned by a simple model. We showed how this idea can be applied to two specific online prediction tasks: what is the next place a user will visit? and how long will he stay in the current place?. Using smartphone data collected from 153 users during 17 months, we show the potential of our method in predicting human mobility in real life. Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object's movements can be represented by some mathematical formulas of motion functions based on its recent movements. However, an object's movements are more complicated than what the mathematical formulas can represent. Prediction based on an object's trajectory patterns is a powerful way and has been investigated by several work. But their main interest is how to discover the patterns. In this paper, we present a novel prediction approach, namely The Hybrid Prediction Model, which estimates an object's future locations based on its pattern information as well as existing motion functions using the object's recent movements. Specifically, an object's trajectory patterns which have ad-hoc forms for prediction are discovered and then indexed by a novel access method for efficient query processing. In addition, two query processing techniques that can provide accurate results for both near and distant time predictive queries are presented. Our extensive experiments demonstrate that proposed techniques are more accurate and efficient than existing forecasting schemes. We investigate opportunistic routing, centering on the recommendation of ideal diversions on trips to a primary destination when an unplanned waypoint, such as a rest stop or a refueling station, is desired. In the general case, an automated routing assistant may not know the driver's final destination and may need to consider probabilities over destinations in identifying the ideal waypoint along with the revised route that includes the waypoint. We consider general principles of opportunistic routing and present the results of several studies with a corpus of real-world trips. Then, we describe how we can compute the expected value of asking a user about the primary destination so as to remove uncertainly about the goal and show how this measure can guide an automated system's engagements with users when making recommendations for navigation and analogous settings in ubiquitous computing.