Spatial-Temporal Dynamic Network (STDN)

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• Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning
• Memory Networks
• 3D Model
• Screening; Passenger, Luggage, & Cargo

Recently, with the advances of deep learning techniques, deep leaning models such as (Deep) Convolutional Neural Network (DCNN/CNN) and Recurrent Neural Network (RNN) have enjoyed considerable success in various machine learning tasks due to their powerful hierarchical feature learning ability in both spatial and temporal domains, and have been widely applied in various spatio-temporal data mining (STDM) tasks such as predictive learning, representation learning, anomaly detection and classification. Deep Learning for Spatio-Temporal Data Mining: A Survey | S. Wang, J. Cao and P. Yu

The key challenge of traffic prediction lies in how to model the complex spatial dependencies and temporal dynamics. Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i.e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical. However, in practice, the spatial dependence could be dynamic (i.e., changing from time to time), and the temporal dynamics could have some perturbation from one period to another period. In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction | H. Yao, X. Tang, H. Wei, G. Zheng, and Z. Li