IOHMM for location prediction with missing data

In recent years, the widespread adoption of GPS enabled vehicles brings the Location Based Services new opportunities. It benefits many related fields such as urban planning, city traffic modeling, personalized recommendations and driving suggestions. The service providers can understand their users better by modeling the mobility pattern and provide more personalized services by predicting the destination of users' travels. In this paper, we propose to model both the temporal and spatial mobility patterns of human movements and predict the user's travel destination from certain origin place at certain time with specific IOHMM. In order to account for data missing, we introduce a dummy state in the process of constructing the IOHMM data sequence. We also demonstrate the possibility to represent individual mobility preference by building the user mobility profiles with the learnt IOHMM. We evaluate the prediction accuracy of our method with two datasets, and the experimental results show that our method outperforms several state-of-the-art works on both datasets.

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