关键词: 位置预测, 降维, 移动对象, 长短期记忆网络（LSTM）

Abstract: Location prediction of moving object is an important part in location based service. Existing location prediction algorithms of moving object include Markov chain, hidden Markov model, neural network, etc. However, existing algorithms cannot solve the problem of dimensionality disaster caused by too many positions of the moving object??s trajectory data. In order to overcome this problem, this paper proposes a location distributed representation model (LDRM). The model reduces the dimension of one-hot vector which represents each location to a low dimension location embedding vector which concludes the moving object??s moving pattern. After that, LDRM is combined with location prediction algorithm based on long short-term memory (LSTM) neural network to get an overall algorithm called LDRM-LSTM. Experiment results on real dataset show that, there has been a major improvement of the LDRM-LSTM algorithm compared with the existing ones, in terms of prediction accuracy.

Key words: location prediction, dimension reduction, moving object, long short-term memory (LSTM)