利用无监督预训练的轨迹深度关联OA北大核心CSTPCD

Due to the limitations of spatiotemporal similarity algorithm for trajectory matching,a deep learning method is adopted and a matching neural network training method based on unsupervised pre-training is proposed.Geohash vector embedding is used to perform feature engineering processing on trajectory signals,a self-attention mechanism neural network structure is built,and unlabeled trajectory data is used to conduct model pre-training based on mask prediction task.Then a siamese matching network structure is constructed and the pre-training model parameters are loaded.Finally,labeled trajectory pair data is used to fine-tune the parameters of the pre-training model based on the mean square error loss function to obtain the trajectory matching model.The Geolife GPS trajectory data is used as the evaluation dataset for model training and testing.The experimental results show that the proposed method improves the matching accuracy by 5 percentage points(reaching 96.3%)compared with the existing optimal algorithm,thus fully demonstrating the effectiveness of the method.At present,there is limited research in the field of trajectory matching based on deep learning pre-training models,and the proposed method has important reference significance.