Trajectory prediction plays an important role in supporting many advanced applications such as location-based services and advanced intelligent traffic managements. Most existing trajectory prediction methods employed fixed spatial division and focused on human closeness movement patterns. However, these methods could lead to a sharp boundary limitation and ignore the periodic characteristics of human mobility. This paper proposes a novel trajectory prediction method based on long short-term memory network (LSTM) called the trajectory predictor with fuzzy-long short-term memory network (TrjPre-FLSTM). First, we introduce a new fuzzy trajectory concept and extend the LSTM to a fuzzy-LSTM to overcome the sharp boundary limitation. Second, we explicitly incorporate the periodic movement patterns of moving objects in the trajectory prediction. Using a real-world mobile phone dataset, we evaluate the performance of TrjPre-FLSTM with two latest competitors. The case study results indicate that the proposed method outperforms the comparative methods in terms of the prediction accuracy.


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    Title :

    Predicting future locations of moving objects with deep fuzzy-LSTM networks


    Contributors:
    Li, Mingxiao (author) / Lu, Feng (author) / Zhang, Hengcai (author) / Chen, Jie (author)

    Published in:

    Publication date :

    2020-12-20


    Size :

    18 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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