@Article{jihpp.2020.016243,
AUTHOR = {Xingxing Cao, Liming Jiang, Xiaoliang Wang, Frank Jiang},
TITLE = {A Location Prediction Method Based on GA-LSTM Networks and Associated Movement Behavior Information},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {187--197},
URL = {http://www.techscience.com/jihpp/v2n4/41150},
ISSN = {2637-4226},
ABSTRACT = {Due to the lack of consideration of movement behavior information 
other than time and location perception in current location prediction methods, 
the movement characteristics of trajectory data cannot be well expressed, which 
in turn affects the accuracy of the prediction results. First, a new trajectory data 
expression method by associating the movement behavior information is given. 
The pre-association method is used to model the movement behavior information 
according to the individual movement behavior features and the group movement 
behavior features extracted from the trajectory sequence and the region. The 
movement behavior features based on pre-association may not always be the best
for the prediction model. Therefore, through association analysis and importance 
analysis, the final association feature is selected from the pre-association features.
The trajectory data is input into the LSTM networks after associated features and 
genetic algorithm (GA) is used to optimize the combination of the length of time 
window and the number of hidden layer nodes. The experimental results show 
that compared with the original trajectory data, the trajectory data associated 
with the movement behavior information helps to improve the accuracy of 
location prediction.},
DOI = {10.32604/jihpp.2020.016243}
}