High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio-Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications.
Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Spatio-Temporal Self-Supervised Learning (ST-SSL) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. Specifically, our ST-SSL is built over an integrated module with temporal and spatial convolutions for encoding the information across space and time. To achieve the adaptive spatio-temporal self-supervised learning, our ST-SSL first performs the adaptive augmentation over the traffic flow graph data at both attribute- and structure-levels. On top of the augmented traffic graph, two SSL auxiliary tasks are constructed to supplement the main traffic prediction task with spatial and temporal heterogeneity-aware augmentation. Experiments on four benchmark datasets demonstrate that ST-SSL consistently outperforms various state-of-the-art baselines. Since spatio-temporal heterogeneity widely exists in practical datasets, the proposed framework may also cast light on other spatial-temporal applications. Model implementation is available at https://github.com/Echo-Ji/ST-SSL.
Introduction: High-resolution data is essential for understanding the complexity of the relationship between the spread of coronavirus disease 2019 (COVID-19), resident behavior, and interventions, which could be used to inform policy responses for future prevention and control.Methods: We obtained high-resolution human mobility data and epidemiological data at the community level. We propose a metapopulation Susceptible-Exposed-Presymptomatic-Infectious-Removal (SEPIR) compartment model to utilize the available data and explore the internal driving forces of COVID-19 transmission dynamics in the city of Wuhan. Additionally, we will assess the effectiveness of the interventions implemented in the smallest administrative units (subdistricts) during the lockdown.Results: In the Wuhan epidemic of March 2020, intra-subdistrict transmission caused 7.6 times more infections than inter-subdistrict transmission. After the city was closed, this ratio increased to 199 times. The main transmission path was dominated by population activity during peak evening hours.Discussion: Restricting the movement of people within cities is an essential measure for controlling the spread of COVID-19. However, it is difficult to contain intra-street transmission solely through citywide mobility restriction policies. This can only be accomplished by quarantining communities or buildings with confirmed cases, and conducting mass nucleic acid testing and enforcing strict isolation protocols for close contacts.
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