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Analyzing the urban trajectory in cities has become an important topic in data mining. How can we model the human mobility consisting of stay and travel states from the raw trajectory data? How can we infer these mobility states from a single user's trajectory information? How can we further generalize the mobility inference to the real-world trajectory data that span multiple users and are sparsely sampled over time? In this article, based on formal and rigid definitions of the stay/travel mobility, we propose a single trajectory inference algorithm that utilizes a generic long-tailed sparsity pattern in the large-scale trajectory data. The algorithm guarantees a 100% precision in the stay/travel inference with a provable lower bound in the recall metric. Furthermore, we design a transformer-like deep learning architecture on the problem of mobility inference from multiple sparse trajectories. Several adaptations from the standard transformer network structure are introduced, including the singleton design to avoid the negative effect of sparse labels in the decoder side, the customized space-time embedding on features of location records, and the mask apparatus at the output side for loss function correction. Evaluations on three trajectory datasets of 40 million urban users validate the performance guarantees of the proposed inference algorithm and demonstrate the superiority of our deep learning model, in comparison to sequence learning methods in the literature. On extremely sparse trajectories, the deep learning model improves from the single trajectory inference algorithm with more than two times of overall and F1 accuracy. The model also generalizes to large-scale trajectory data from different sources with good scalability. [ABSTRACT FROM AUTHOR]

Traffic prediction is a complex, nonlinear spatiotemporal relationship modeling task with the randomness of traffic demand, the spatial and temporal dependency between traffic flows, and other recurrent and nonrecurrent factors. Based on the ability to learn generic features from history information, deep learning approaches have been recently applied to traffic prediction. Convolutional neural network (CNN) methods that learn traffic as images can improve the predictive accuracy by leveraging the implicit correlations among nearby links. Traffic prediction based on CNN is still in its initial stage without making full use of spatiotemporal traffic information. In this paper, we improve the predictive accuracy by directly capturing the relationship between the input sequence and the predicted value. We propose the new local receptive fields for spatiotemporal traffic information to provide the constraints in the task domain for CNN which is different from traditionally learning traffic as images. We explore a max-pooled CNN followed by a fully connected layer with a nonlinear activation function to convolute the new local receptive fields. The higher global-level features are fed into a predictor to generate the predicted output. Based on the dataset provided by Highways England, we validate the assumption that there exists direct relationship between the input sequence and the predicted value. We train the proposed method by using the backpropagation approach, and we employ the AdaGrad method to update the parameters of the proposed method. The experimental results show that the proposed method can improve the predictive accuracy. [ABSTRACT FROM AUTHOR]

Deep learning approaches have been recently applied to traffic prediction because of their ability to extract features of traffic data. While convolutional neural networks may improve the predictive accuracy by transiting traffic data to images and extracting features in the images, the convolutional results can be improved by using the global-level representation that is a direct way to extract features. The time intervals are not considered as aspects of convolutional neural networks for traffic prediction. The attention mechanism may adaptively select a sequence of regions and only process the selected regions to better extract features when aspects are considered. In this paper, we propose the attention mechanism over the convolutional result for traffic prediction. The proposed method is based on multiple links. The time interval is considered as the aspect of attention mechanism. Based on the dataset provided by Highways England, the experimental results show that the proposed method can achieve better accuracy than the baseline methods. [ABSTRACT FROM AUTHOR]