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Human mobility prediction is a key task in smart cities to help improve urban management effectiveness. However, it remains challenging due to widespread intractable noises in large-scale mobility data. Based on previous research and our statistical analysis of real large-scale data, we observe that there is heterogeneity in the quality of users’ trajectories, that is, the regularity and periodicity of one user’s trajectories can be quite different from another. Inspired by this, we propose a trajectory quality calibration framework for quantifying the quality of each trajectory and promoting high-quality training instances to calibrate the final prediction process. The main module of our approach is a calibration network that evaluates the quality of each user’s trajectories by learning their similarity between them. It is designed to be model-independent and can be trained in an unsupervised manner. Finally, the mobility prediction model is trained with the instance-weighting strategy, which integrates quantified quality scores into the parameter updating process of the model. Experiments conducted on two citywide mobility datasets demonstrate the effectiveness of our approach when dealing with massive noisy trajectories in the real world.

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