Next place prediction using mobile data (original) (raw)

Proceedings of the 2009 international conference on Pervasive services - ICPS '09, 2009

Mobile context-aware applications experience a constantly changing environment with increased dynamicity. In order to work efficiently, the location of mobile users needs to be predicted and properly exploited by mobile applications. We propose a spatial context model, which deals with the location prediction of mobile users. Such model is used for the classification of the users' trajectories through Machine Learning (ML) algorithms. Predicting spatial context is treated through supervised learning. We evaluate our model in terms of prediction accuracy w.r.t. specific prediction parameters. The proposed model is also compared with other ML algorithms for location prediction. Our findings are very promising for the efficient operation of mobile context-aware applications.

Predicting the Location of Mobile Users A Machine Learning Approach

Next Loc-An Approach for Mobile users Future Location Prediction

International Journal of Wireless Communications and Network Technologies, 2019

Faster development in cellular networks provides faster and seamless services to mobile users. Mobility management is an important issue in the area of mobile communication. Within the cellular networks, movement track of mobile user is provided by the location management [1]. Location of mobile station has great attention and has potential for application and services to improve location-based services like prediction of the next location. Several researchers are worked to develop methods and algorithms which increase the positioning accuracy and prediction rate. One of the powerful features of modern Smartphone's is their ability to provide us with real-time, location based information such as weather updates, traffic, time to reach and so on..In this paper,a proposed system that can predict a user's next location using their location history, current location and the current time, has been developed.

Predicting the next location change and time of change for mobile phone users

Proceedings of the Third ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems, 2014

Predicting the next location of people from their mobile phone logs has become an active research area. Due to two main reasons this problem is very challenging: the log data is very large and there are variety of granularity levels for specifying the spatial and the temporal attributes. In this work, we focus on predicting the next location change of the user and when this change occurs. Our method has two steps, namely clustering the spatial data into larger regions and grouping temporal data into time intervals to get higher granularity levels, and then, applying sequential pattern mining technique to extract frequent movement patterns to predict the change of the region of the user and its time frame. We have validated our results with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, and we have obtained very high accuracy results.

Advanced Location Prediction Techniques in Mobile Computing

Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. Firstly, we propose an efficient spatial context classifier and a short-term predictor for the future location of a mobile user in cellular networks. Secondly, we propose a novel adaptive mobility prediction algorithm, which deals with location context representation and trajectory prediction of moving users. Thirdly, we propose a short-memory adaptive location predictor that realizes mobility prediction in the absence of extensive historical mobility information. Fourthly, we assume the existence of a pattern base and try to compare the movement pattern of a certain user with stored information in order to predict future locations. Our findings, compared with other schemes, are very promising for the location prediction problem and the adoption of proactive context-aware applications and services.

Context Specific Next Location Prediction

2014

The rapid evolution and proliferation of mobile devices, mobile Internet access and social networks facilitate the development of intelligent services that build on the knowledge of the next location of a mobile user. The prediction of the next location of a mobile user, among others based on spatial, temporal, and in particular social influence factors, is the subject of this dissertation.

User location forecasting at points of interest

Proceedings of the 2012 RecSys workshop on Personalizing the local mobile experience - LocalPeMA '12, 2012

Predicting the location of a mobile user in the near future can be used for a very large number of user-centered or crowd-centered ubiquitous applications. It is convenient for the discussion to think in terms of discrete locations driven by Points of Interest (POI) instead of absolute positions. We postulate that POI sequences are Markovian once the data is clustered by day of the week and time of the day. To prove our hypothesis we used a public dataset, used in a previous work [15]. In that paper the authors were able to predict the location of a user with 90% to 70% accuracy in five minutes and one hour time windows, respectively. With our approach, using Hidden Markov Models, we are able to predict the next POIs within seven hours without significant accuracy decrease. This result enables a large number of potential applications where the aggregate data of a single users conform the behavior of the crowd.

A data mining approach for location prediction in mobile environments

Data & Knowledge Engineering, 2005

Mobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this paper, we propose a new algorithm for predicting the next inter-cell movement of a mobile user in a Personal Communication Systems network. In the first phase of our threephase algorithm, user mobility patterns are mined from the history of mobile user trajectories. In the second phase, mobility rules are extracted from these patterns, and in the last phase, mobility predictions are accomplished by using these rules. The performance of the proposed algorithm is evaluated through simulation as compared to two other prediction methods. The performance results obtained in terms of Precision and Recall indicate that our method can make more accurate predictions than the other methods.

A method for predicting future location of mobile user for location-based services system

Comput. Ind. Eng., 2009

Convergence of location-aware devices, wireless communications, and geographic information system (GIS) functionalities has been enabling the deployment of a new generation of selective information disseminating services and location-based services (LBSs). Current LBSs use information about current locations of users to provide services, such as nearest features of interest, they request. Although the common computing strategy in LBSs benefits the users, there are additional benefits when future locations are predicted. One major advantage of location prediction is that it provides LBSs with extended resources, mainly time, to improve system reliability which in turn increases the users' confidence and the demand for LBSs. In this study, we propose a movement Rule-based Location Prediction method (RLP), to guess the user's future location for LBSs. Its performance is assessed with respect to precision and recall. In comparison with the previous technique, the prediction accu...

Predict Next User Location to Improve Accuracy of Mobile Advertising

Journal of Physics: Conference Series

The main feature in the location based on advertising is sending the latest ads from POIs nearest the user. To accomplish the task, the device should transmit the current location at interval time to LBA server, LBA server will provide the latest ads nearest location received from the user. With short interval this procedure will run out the power of a device quickly and high internet bandwidth. To solve this long interval can be used. But another problem arises with fast moving users. User received POIs at a location that has been leaved. We proposed predict next user's location for long interval push strategy. The results of our experiments show received POIs more accurate and with long interval will help reduce waste of energy and internet bandwidth in mobile advertising.

Next place prediction using mobile data (original) (raw)

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