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- W1984916170 abstract "We propose scalable next location prediction model using temporal, movement behavior and trajectory history information. Most of the existing models use the whole information database and do not consider temporal information for location prediction; lead to scalability and accuracy issues. We consider road topology, multiple predictive factors and temporal information such as day and time to improve the accuracy and scalability issues. We extend the existing Mobility Markov Chain (MMC) model to incorporate n previously visited locations have movement factors mk in the time interval t of the day that we coined nmt- MMC. We use the whole information database in making the model in offline, and find similarity with the model for location and trajectory prediction in online. Simulation results demonstrate the trade-off between scalability and accuracy, and the effect of Top-N similar trajectories on the accuracy. We found that incorporating movement predictive factors improves the accuracy by approximately 7-percent, and adding both predictive factors and temporal information improve the accuracy by 14-percent." @default.
- W1984916170 created "2016-06-24" @default.
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- W1984916170 date "2013-01-01" @default.
- W1984916170 modified "2023-09-26" @default.
- W1984916170 title "Temporal-aware Location Prediction Model Using Similarity Approach" @default.
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- W1984916170 doi "https://doi.org/10.2991/3ca-13.2013.60" @default.
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