DeepFR: A trajectory prediction model based on deep feature representation

Abstract

Mining key information from trajectory data can effectively help people in their life. In the case of hurricanes, trajectory prediction can avoid losses caused by disasters. Deep learning has widely used in many fields. However, trajectory is simply information such as longitude and latitude. It is difficult to extract trajectory features using deep learning. Therefore, a trajectory prediction model based on deep feature representation (DeepFR) is proposed by extracting various features. The model consists of trajectory data preprocessing layer, deep feature representation layer and trajectory prediction layer. The trajectory data preprocessing layer divides a trajectory into subtrajectories by using a sliding window. The deep feature extraction layer performs feature extraction for each subtrajectory. The spatiotemporal feature and 3D dynamic feature of each subtrajectory are extracted using bidirectional long short-term memory network (BiLSTM) and 3D convolutional neural network (3DCNN) respectively. The trajectory prediction layer first uses the self-attention to assign different weight to the feature of different subtrajectories. Then, latitude and longitude are predicted simultaneously through multitask learning. The real hurricane and typhoon data are used for simulation experiments. It is found that the DeepFR model has the best prediction effect and is more accurate and stable in long term prediction.