Exploring Complex Dependencies for Multi-modal Semantic Trajectory Prediction

Multi-modal semantic trajectory prediction is of great importance for location-based applications. However, predicting trajectory is not trivial facing three challenges: (1) It is difficult to integrate useful information from multi-modal and heterogeneous data in different granularity for effective feature fusion; (2) All kinds of dependencies existing in multi-modal semantic trajectories are closely coupled and dynamically evolved, forming complex dependencies for which are difficult to quantify; (3) During the model training, the distribution of each modal feature shifts in different directions, resulting in the distortion of dependencies, which is accompanied by slow convergence and inaccurate predictions. In this paper, the Complex Dependencies Auto-learning Prediction Model (CDAPM) is proposed to solve these problems. First, the effective and robust representation of each points is obtained by jointly embedding multi-modal information. Then, the dependencies attention module is proposed to calculate the dependencies weight matrix and auto-learn the contribution of each point. Also, it solves the problem of long-term dependency effectively. Position Encoding and LSTM are used to enhance the time relationship of trajectory. Finally, Mode Normalization is designed to maintain prediction accuracy by preventing the distortion of dependencies and significantly accelerate the convergence speed. Experiments on two real data sets show that CDAPM outperforms the state-of-the-art methods.